Prediction of quality and consumers’ preferences is essential task for food producers to improve their
market share and reduce any gap in food safety standards. In this paper, we develop a machine learning
method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images
using image processing texture and color feature extraction techniques. We compare three unsupervised
ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and
t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique
(Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the
supervised ML feature selection technique over the traditional feature selection techniques.
In this paper, we propose an easy approach of
identification and classification of high calorie snacks for dietary
assessment using machine learning. As an object detection
technique we have use point features matching algorithm to
identify the object of interest from a cluttered scene. After
detecting the object, a Bag of Features (BoF) model is created by
extracting Speed up Robust features (SURF) features. This BoF
model is used to recognize and classify the snacks items of different
categories. We have used three types of snacks images named: Icecream,
Chips and Chocolate for experimental purpose. Depending
on the experimental results our proposed algorithm is able to
detect and classify different types of snacks with around 85%
accuracy.
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.
Keywords: chemometric analysis; transgenic agricultural products and foods; near-infrared spectroscopy; hyperspectral imaging
1. Introduction
Currently, genetics is widely used in various science fields. Transgenic technology is used to transfer the genes with known functional traits, such as high yield, resistance to disease and insects, and improvement of nutritional quality, into the target organism through modern scientific and technological means, so that new varieties and products are produced by adding new functional characteristics to the recipient organism. Many countries regard transgenic technology as a strategic choice to support development. Transgenics has become a strategic focus for countries to seize the commanding heights of science and technology, and to enhance the international competitiveness of agriculture.
At present, applications of transgenic technology in various fields, including to improve crops, produce vaccines, food, etc., are experiencing a very high growth rate. As a result, genetically modified (GM) production is increasing on the global market. Genetically modified crops are cultivated in 29 countries, with an area under cultivation of 190.4 million hectares [1]. Although GM crops have advantages such as insect resistance, weed resistance, disease resistance, improved nutritional value, and increased yield [2], the use of GM technology may have unintended negative effects on food and environmental safety, and therefore, GM foods have been severely restricted in most parts of the world due to legal pressure from regulatory agencies to control the production of GM products. Thus, it is a very necessary and important task to identify GM products.
Today, several me
Mass estimation of citrus limetta using distance based hand crafted features...IJECEIAES
Sorting and grading are qualitative operational tasks performed in food processing industries. Realization of higher accuracy in mass estimation is the key inclination of this work. In this work, an automated technique for mass estimation of citrus limetta is devised based on the geometrical features derived from pre-processed images. Dataset includes 250 data samples of citrus limetta, whose images are acquired in different orientations. Two novel handcrafted distance-based geometrical features along with four conventional geometrical features were employed for regression analysis. Predictive modeling is conducted with configuration of 150 training and 100 testing data samples and subject to regression analysis for mass estimation. Multiple linear and support vector regression models with linear, polynomial and radial basis function (RBF) kernels were employed for mass estimation with two different model configurations, conventional and conventional with handcrafted features, for which an R2 score of 0.9815, root mean squared error (RMSE) of 10.94 grams, relative averages of accuracy and error of 96.61% and 3.39% respectively is achieved for the proposed model and configuration which was validated using k-fold cross-validation. Through comparison with performance of model with conventional and conventional with handcrafted features configurations, it was established that inclusion of handcrafted features was able to increase the performance of the models.
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with A...Damian R. Mingle, MBA
Rice continues to be a primary food for the world’s population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyperparameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of
the proposed approach for rice disease detection and treatments.
In this paper, we propose an easy approach of
identification and classification of high calorie snacks for dietary
assessment using machine learning. As an object detection
technique we have use point features matching algorithm to
identify the object of interest from a cluttered scene. After
detecting the object, a Bag of Features (BoF) model is created by
extracting Speed up Robust features (SURF) features. This BoF
model is used to recognize and classify the snacks items of different
categories. We have used three types of snacks images named: Icecream,
Chips and Chocolate for experimental purpose. Depending
on the experimental results our proposed algorithm is able to
detect and classify different types of snacks with around 85%
accuracy.
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.
Keywords: chemometric analysis; transgenic agricultural products and foods; near-infrared spectroscopy; hyperspectral imaging
1. Introduction
Currently, genetics is widely used in various science fields. Transgenic technology is used to transfer the genes with known functional traits, such as high yield, resistance to disease and insects, and improvement of nutritional quality, into the target organism through modern scientific and technological means, so that new varieties and products are produced by adding new functional characteristics to the recipient organism. Many countries regard transgenic technology as a strategic choice to support development. Transgenics has become a strategic focus for countries to seize the commanding heights of science and technology, and to enhance the international competitiveness of agriculture.
At present, applications of transgenic technology in various fields, including to improve crops, produce vaccines, food, etc., are experiencing a very high growth rate. As a result, genetically modified (GM) production is increasing on the global market. Genetically modified crops are cultivated in 29 countries, with an area under cultivation of 190.4 million hectares [1]. Although GM crops have advantages such as insect resistance, weed resistance, disease resistance, improved nutritional value, and increased yield [2], the use of GM technology may have unintended negative effects on food and environmental safety, and therefore, GM foods have been severely restricted in most parts of the world due to legal pressure from regulatory agencies to control the production of GM products. Thus, it is a very necessary and important task to identify GM products.
Today, several me
Mass estimation of citrus limetta using distance based hand crafted features...IJECEIAES
Sorting and grading are qualitative operational tasks performed in food processing industries. Realization of higher accuracy in mass estimation is the key inclination of this work. In this work, an automated technique for mass estimation of citrus limetta is devised based on the geometrical features derived from pre-processed images. Dataset includes 250 data samples of citrus limetta, whose images are acquired in different orientations. Two novel handcrafted distance-based geometrical features along with four conventional geometrical features were employed for regression analysis. Predictive modeling is conducted with configuration of 150 training and 100 testing data samples and subject to regression analysis for mass estimation. Multiple linear and support vector regression models with linear, polynomial and radial basis function (RBF) kernels were employed for mass estimation with two different model configurations, conventional and conventional with handcrafted features, for which an R2 score of 0.9815, root mean squared error (RMSE) of 10.94 grams, relative averages of accuracy and error of 96.61% and 3.39% respectively is achieved for the proposed model and configuration which was validated using k-fold cross-validation. Through comparison with performance of model with conventional and conventional with handcrafted features configurations, it was established that inclusion of handcrafted features was able to increase the performance of the models.
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with A...Damian R. Mingle, MBA
Rice continues to be a primary food for the world’s population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyperparameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of
the proposed approach for rice disease detection and treatments.
An evaluation of machine learning algorithms coupled to an electronic olfact...IJECEIAES
The aim of this investigatation is to compare the utility of machine learning algorithms in distinguishing between untreated and processed mint beside in predicting the spray day of the insecticide. Within seven days, mint treated samples with the malathion insecticide are collected, and their aromas are Studied using a laboratory-manufactured sensor array system based on commercial metallic semiconductor (MOS) gas sensors. To distinguish the mint type, some results of machine learning algorithms were compared to know the decision trees (DT), Naive Bayes, support vector machines (SVM), and ensemble classifier. Furthermore, to predict the treatment day support vector machines regression (SVMR) and partial least squares regression (PLSR) were compared. Regarding the best results, in the discrimination case, a success rate of 92.9% was achieved by the ensemble classifier while in the prediction case, a correlation coefficient of R=0.82 was reached by the SVMR. Good results are achieved if the right gas sensor array system is designed and realized coupled with a good choice of the appropriate machine learning algorithms.
Multivariate regression methods with infrared spectroscopy to detect the fals...IJRTEMJOURNAL
Recently, food safety and guaranteed of food marks have become more important subjects of
foodstuff production and the marketing of processed foods. This paper demonstrates the ability of Mid Infrared
spectroscopy coupled with multivariate regression tools to detect vegetable butter (as adulterant) in a binary
mixture with traditional cow’s butter. Blends of traditional cow’s butter with different percentages of vegetable
butter were measured using Attenuated Total Reflectance-Fourier Transform Mid Infrared Spectroscopy (ATRFTMIR). Spectral and reference data were firstly analyzed by principal component analysis (PCA) to check
outliers samples; and improve the robustness of the prediction models to be established. Multivariate regression
methods as Principal component regression (PCR) and Partial least square regression (PLSR) were used to
establish calibration model. Excellent correlation between ATR-FTMIR analysis and studied butter blends was
obtained R2 = 0.99; with Root Mean Square Errors of Prediction < 3.04, Limit of Detection 9.12% (By PCR)
and 6.06% (by PLSR), and Relative Prediction Errors as low as 3.13.
An Analysis of Tourism Competitiveness Index of Europe and Caucasus: A Study ...IJRTEMJOURNAL
This study aims to find the association-ship between the Regional Rank of the Travel and
Tourism Competitiveness Index and its Indicators in 37 European countries. The cross-sectional data of the 37
European countries are collected from the World Economic Forum report- 2015. The statistical software
package, SPSS v. 20.0 is used to analyze the data. ANOVA (Analysis of Variance), Multi-co-linearity, Multiple
Regression, and Residual Analysis are the tools used to analyze to achieve out the objective of the study. RR:
Regional Rank of the Travel and Tourism Competitiveness Index is used as the dependent variable and TI:
Tourism Services Infrastructure, GP: Ground & Port Infrastructure, BE: Business Environment, PT:
Prioritization of Travel and Tourism, and CR: Cultural resources & business travel are used as the independent
variables. It is found that there is an inverse relationship between the dependent variable and all the
independent variables along with the statistical significance. It is recommended that the governments of the
European countries and the respective agents of these countries should be made aware of learning the findings
of this study to promote their countries which can be victorious in lowering their Regional Rank of the Travel
and Tourism Competitiveness Index
528Seed Technological Development – A Surveyidescitation
This paper provides a review of automating or semi-automating the seed quality
purity test. Computer vision (CV) technology used in variety of industries is a sophisticated
type of inspection technology; however, it is not widely used in agriculture.The application
of CV technologies is very challenging in agriculture. As CV plays an important role in this
domain, research in this area has been motivated. Several theories of automating seed
quality purity test are briefly mentioned. The reviewed approaches are classified according
to features and classifiers. The methods for extracting features of a particular seed, and the
classifiers used for classifying the seeds, are mentioned in the paper. An overview of the
most representative methods for feature extraction and classification of seeds is presented.
The major goal of the paper is to provide a comprehensive reference source for the
researchers involved in automation of seed classification, regardless of particular feature or
classifier.
Classification of Macronutrient Deficiencies in Maize Plant Using Machine Lea...IJECEIAES
Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in nondestructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets.
Submerged fermentation of laccase producing Streptomyces chartreusis using bo...IOSR Journals
Response surface methodology was engaged for the optimization of diverse nutritional and physical parameters for laccase production by Streptomyces chartreusis strain NBRC 12753 in the submerged fermentation process. Screening of production parameters was executed using Plackett–Burman design and the variables with statistically momentous effects on laccase production were recognized. Variables such as Cupric sulphate, Pyrogallol and Yeast extract were selected for further optimization studies using Box-Behnken design. The multiple regression coefficients (R2) had a value of 0.9606, indicating that the model could explain up to 96.06 % of the variability of the response. This methodology facilitated analysis of the experimental data to establish the optimum conditions for the process and understand the contribution of individual factors to evaluate the response under optimal conditions. Thus application of Box-Behnken approach appears to have potential usage in process application.
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has suppli
ed a large volume of data to many fields. The gene
microarray analysis and classification have demonst
rated an effective way for the effective diagnosis
of
diseases and cancers. In as much as the data achiev
ing from microarray technology is very noisy and al
so
has thousands of features, feature selection plays
an important role in removing irrelevant and redund
ant
features and also reducing computational complexity
. There are two important approaches for gene
selection in microarray data analysis, the filters
and the wrappers. To select a concise subset of inf
ormative
genes, we introduce a hybrid feature selection whic
h combines two approaches. The fact of the matter i
s
that candidate’s features are first selected from t
he original set via several effective filters. The
candidate
feature set is further refined by more accurate wra
ppers. Thus, we can take advantage of both the filt
ers
and wrappers. Experimental results based on 11 micr
oarray datasets show that our mechanism can be
effected with a smaller feature set. Moreover, thes
e feature subsets can be obtained in a reasonable t
ime
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has supplied a large volume of data to many fields. The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. In as much as the data achieving from microarray technology is very noisy and also has thousands of features, feature selection plays an important role in removing irrelevant and redundant features and also reducing computational complexity. There are two important approaches for gene selection in microarray data analysis, the filters and the wrappers. To select a concise subset of informative genes, we introduce a hybrid feature selection which combines two approaches. The fact of the matter is that candidate’s features are first selected from the original set via several effective filters. The candidate feature set is further refined by more accurate wrappers. Thus, we can take advantage of both the filters and wrappers. Experimental results based on 11 microarray datasets show that our mechanism can be effected with a smaller feature set. Moreover, these feature subsets can be obtained in a reasonable time.
Two degree of freedom PID based inferential control of continuous bioreactor ...ISA Interchange
This article presents the development of inferential control scheme based on Adaptive linear neural network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy of ADALINE is enhanced by retraining it with immediate past measurements. The ADALINE and retrained ADALINE are used along with PID and 2-DOF-PID leading to APID, A2PID, RAPID and RA2PID inferential controllers. Further the parameters of 2-DOF-PID are optimized using Non-dominated sorted genetic algorithm-II and used with retrained ADALINE soft sensor which leads to RAN2PID inferential controller. Simulation results demonstrate that performance of proposed RAN2PID controller is better in comparison to other designed controllers in terms of qualitative and quantitative performance indices.
Growth Characteristics Modeling of Lactobacillus acidophilus using RSM and ANNGanga Sahay Meena
The culture conditions viz. additional carbon and n
itrogen content, inoculum size, age, temperature an
d pH of
Lactobacillus acidophilus
were optimized using response surface methodology (
RSM) and artificial neural network
(ANN). Kinetic growth models were fitted to cultiva
tions from a Box-Behnken Design (BBD) design experi
ments for
different variables. This concept of combining the
optimization and modeling presented different optim
al conditions
for
L. acidophilus
growth from their original optimization study. Thr
ough these statistical tools, the product yield
(cell mass) of
L. acidophilus
was increased. Regression coefficients (R
2
) of both the statistical tools predicted that
ANN was better than RSM and the regression equation
was solved with the help of genetic algorithms (GA
). The
normalized percentage mean squared error obtained f
rom the ANN and RSM models were 0.06 and 0.2%,
respectively. The results demonstrated a higher pre
diction accuracy of ANN compared to RSM
Precision agriculture relies heavily on information technology, which also aids agronomists in their work. Weeds usually grow alongside crops, reducing the production of that crop. They are controlled by herbicides. The pesticide may harm the crop as well if the type of weed is not identified. To control weeds on farms, it is required to identify and classify them. A convolutional network or CNN, a deep learning-based computer vision technology, is used to evaluate images. A methodology is proposed to detect weeds using convolutional neural networks. There were two primary phases in this proposed methodology. The first phase is image collection and labeling, in which the features for images to be labeled for the base images are extracted. In the second phase, the convolutional neural network model is constructed by 20 layers to detect the weed. CNN architecture has three layers, namely, the convolutional layer, the pooling layer, and the dense layer. The input image is given to a convolutional layer to extract the features from the image. The features are given to the pooling layer to compress the image to reduce the computational complexity. The dense layer is used for final classification. The performance of the proposed methodology is assessed using agricultural dataset images taken from the Kaggle database.
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...thanhdowork
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
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An evaluation of machine learning algorithms coupled to an electronic olfact...IJECEIAES
The aim of this investigatation is to compare the utility of machine learning algorithms in distinguishing between untreated and processed mint beside in predicting the spray day of the insecticide. Within seven days, mint treated samples with the malathion insecticide are collected, and their aromas are Studied using a laboratory-manufactured sensor array system based on commercial metallic semiconductor (MOS) gas sensors. To distinguish the mint type, some results of machine learning algorithms were compared to know the decision trees (DT), Naive Bayes, support vector machines (SVM), and ensemble classifier. Furthermore, to predict the treatment day support vector machines regression (SVMR) and partial least squares regression (PLSR) were compared. Regarding the best results, in the discrimination case, a success rate of 92.9% was achieved by the ensemble classifier while in the prediction case, a correlation coefficient of R=0.82 was reached by the SVMR. Good results are achieved if the right gas sensor array system is designed and realized coupled with a good choice of the appropriate machine learning algorithms.
Multivariate regression methods with infrared spectroscopy to detect the fals...IJRTEMJOURNAL
Recently, food safety and guaranteed of food marks have become more important subjects of
foodstuff production and the marketing of processed foods. This paper demonstrates the ability of Mid Infrared
spectroscopy coupled with multivariate regression tools to detect vegetable butter (as adulterant) in a binary
mixture with traditional cow’s butter. Blends of traditional cow’s butter with different percentages of vegetable
butter were measured using Attenuated Total Reflectance-Fourier Transform Mid Infrared Spectroscopy (ATRFTMIR). Spectral and reference data were firstly analyzed by principal component analysis (PCA) to check
outliers samples; and improve the robustness of the prediction models to be established. Multivariate regression
methods as Principal component regression (PCR) and Partial least square regression (PLSR) were used to
establish calibration model. Excellent correlation between ATR-FTMIR analysis and studied butter blends was
obtained R2 = 0.99; with Root Mean Square Errors of Prediction < 3.04, Limit of Detection 9.12% (By PCR)
and 6.06% (by PLSR), and Relative Prediction Errors as low as 3.13.
An Analysis of Tourism Competitiveness Index of Europe and Caucasus: A Study ...IJRTEMJOURNAL
This study aims to find the association-ship between the Regional Rank of the Travel and
Tourism Competitiveness Index and its Indicators in 37 European countries. The cross-sectional data of the 37
European countries are collected from the World Economic Forum report- 2015. The statistical software
package, SPSS v. 20.0 is used to analyze the data. ANOVA (Analysis of Variance), Multi-co-linearity, Multiple
Regression, and Residual Analysis are the tools used to analyze to achieve out the objective of the study. RR:
Regional Rank of the Travel and Tourism Competitiveness Index is used as the dependent variable and TI:
Tourism Services Infrastructure, GP: Ground & Port Infrastructure, BE: Business Environment, PT:
Prioritization of Travel and Tourism, and CR: Cultural resources & business travel are used as the independent
variables. It is found that there is an inverse relationship between the dependent variable and all the
independent variables along with the statistical significance. It is recommended that the governments of the
European countries and the respective agents of these countries should be made aware of learning the findings
of this study to promote their countries which can be victorious in lowering their Regional Rank of the Travel
and Tourism Competitiveness Index
528Seed Technological Development – A Surveyidescitation
This paper provides a review of automating or semi-automating the seed quality
purity test. Computer vision (CV) technology used in variety of industries is a sophisticated
type of inspection technology; however, it is not widely used in agriculture.The application
of CV technologies is very challenging in agriculture. As CV plays an important role in this
domain, research in this area has been motivated. Several theories of automating seed
quality purity test are briefly mentioned. The reviewed approaches are classified according
to features and classifiers. The methods for extracting features of a particular seed, and the
classifiers used for classifying the seeds, are mentioned in the paper. An overview of the
most representative methods for feature extraction and classification of seeds is presented.
The major goal of the paper is to provide a comprehensive reference source for the
researchers involved in automation of seed classification, regardless of particular feature or
classifier.
Classification of Macronutrient Deficiencies in Maize Plant Using Machine Lea...IJECEIAES
Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in nondestructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets.
Submerged fermentation of laccase producing Streptomyces chartreusis using bo...IOSR Journals
Response surface methodology was engaged for the optimization of diverse nutritional and physical parameters for laccase production by Streptomyces chartreusis strain NBRC 12753 in the submerged fermentation process. Screening of production parameters was executed using Plackett–Burman design and the variables with statistically momentous effects on laccase production were recognized. Variables such as Cupric sulphate, Pyrogallol and Yeast extract were selected for further optimization studies using Box-Behnken design. The multiple regression coefficients (R2) had a value of 0.9606, indicating that the model could explain up to 96.06 % of the variability of the response. This methodology facilitated analysis of the experimental data to establish the optimum conditions for the process and understand the contribution of individual factors to evaluate the response under optimal conditions. Thus application of Box-Behnken approach appears to have potential usage in process application.
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has suppli
ed a large volume of data to many fields. The gene
microarray analysis and classification have demonst
rated an effective way for the effective diagnosis
of
diseases and cancers. In as much as the data achiev
ing from microarray technology is very noisy and al
so
has thousands of features, feature selection plays
an important role in removing irrelevant and redund
ant
features and also reducing computational complexity
. There are two important approaches for gene
selection in microarray data analysis, the filters
and the wrappers. To select a concise subset of inf
ormative
genes, we introduce a hybrid feature selection whic
h combines two approaches. The fact of the matter i
s
that candidate’s features are first selected from t
he original set via several effective filters. The
candidate
feature set is further refined by more accurate wra
ppers. Thus, we can take advantage of both the filt
ers
and wrappers. Experimental results based on 11 micr
oarray datasets show that our mechanism can be
effected with a smaller feature set. Moreover, thes
e feature subsets can be obtained in a reasonable t
ime
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has supplied a large volume of data to many fields. The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. In as much as the data achieving from microarray technology is very noisy and also has thousands of features, feature selection plays an important role in removing irrelevant and redundant features and also reducing computational complexity. There are two important approaches for gene selection in microarray data analysis, the filters and the wrappers. To select a concise subset of informative genes, we introduce a hybrid feature selection which combines two approaches. The fact of the matter is that candidate’s features are first selected from the original set via several effective filters. The candidate feature set is further refined by more accurate wrappers. Thus, we can take advantage of both the filters and wrappers. Experimental results based on 11 microarray datasets show that our mechanism can be effected with a smaller feature set. Moreover, these feature subsets can be obtained in a reasonable time.
Two degree of freedom PID based inferential control of continuous bioreactor ...ISA Interchange
This article presents the development of inferential control scheme based on Adaptive linear neural network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy of ADALINE is enhanced by retraining it with immediate past measurements. The ADALINE and retrained ADALINE are used along with PID and 2-DOF-PID leading to APID, A2PID, RAPID and RA2PID inferential controllers. Further the parameters of 2-DOF-PID are optimized using Non-dominated sorted genetic algorithm-II and used with retrained ADALINE soft sensor which leads to RAN2PID inferential controller. Simulation results demonstrate that performance of proposed RAN2PID controller is better in comparison to other designed controllers in terms of qualitative and quantitative performance indices.
Growth Characteristics Modeling of Lactobacillus acidophilus using RSM and ANNGanga Sahay Meena
The culture conditions viz. additional carbon and n
itrogen content, inoculum size, age, temperature an
d pH of
Lactobacillus acidophilus
were optimized using response surface methodology (
RSM) and artificial neural network
(ANN). Kinetic growth models were fitted to cultiva
tions from a Box-Behnken Design (BBD) design experi
ments for
different variables. This concept of combining the
optimization and modeling presented different optim
al conditions
for
L. acidophilus
growth from their original optimization study. Thr
ough these statistical tools, the product yield
(cell mass) of
L. acidophilus
was increased. Regression coefficients (R
2
) of both the statistical tools predicted that
ANN was better than RSM and the regression equation
was solved with the help of genetic algorithms (GA
). The
normalized percentage mean squared error obtained f
rom the ANN and RSM models were 0.06 and 0.2%,
respectively. The results demonstrated a higher pre
diction accuracy of ANN compared to RSM
Precision agriculture relies heavily on information technology, which also aids agronomists in their work. Weeds usually grow alongside crops, reducing the production of that crop. They are controlled by herbicides. The pesticide may harm the crop as well if the type of weed is not identified. To control weeds on farms, it is required to identify and classify them. A convolutional network or CNN, a deep learning-based computer vision technology, is used to evaluate images. A methodology is proposed to detect weeds using convolutional neural networks. There were two primary phases in this proposed methodology. The first phase is image collection and labeling, in which the features for images to be labeled for the base images are extracted. In the second phase, the convolutional neural network model is constructed by 20 layers to detect the weed. CNN architecture has three layers, namely, the convolutional layer, the pooling layer, and the dense layer. The input image is given to a convolutional layer to extract the features from the image. The features are given to the pooling layer to compress the image to reduce the computational complexity. The dense layer is used for final classification. The performance of the proposed methodology is assessed using agricultural dataset images taken from the Kaggle database.
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A MACHINE LEARNING METHOD FOR PREDICTION OF YOGURT QUALITY AND CONSUMERS PREFERENCES USING SENSORY ATTRIBUTES AND IMAGE PROCESSING TECHNIQUES
1. Machine Learning and Applications: An International Journal (MLAIJ) Vol.10, No.1, March 2023
DOI:10.5121/mlaij.2023.10101 1
A MACHINE LEARNING METHOD FOR PREDICTION
OF YOGURT QUALITY AND CONSUMERS
PREFERENCES USING SENSORY ATTRIBUTES AND
IMAGE PROCESSING TECHNIQUES
Maha Hany1
, Shaheera Rashwan1
and Neveen M. Abdelmotilib2
1
Informatics Research Institute, City of Scientific Research and Technological Applications, New
Borg Elarab, Alexandria, Egypt
2
Arid Lands Cultivation Research Institute, City of Scientific Research and Technological
Applications, New Borg Elarab, Alexandria, Egypt
ABSTRACT
Prediction of quality and consumers’ preferences is essential task for food producers to improve their
market share and reduce any gap in food safety standards. In this paper, we develop a machine learning
method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images
using image processing texture and color feature extraction techniques. We compare three unsupervised
ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and
t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique
(Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the
supervised ML feature selection technique over the traditional feature selection techniques.
KEYWORDS
Food quality, Supervised feature selection, Yogurt preferences prediction, random forest classification
1. INTRODUCTION
Food quality assessment is taken from food attributes that fulfil the needs of the consumer,
provide the user with the important nutrients and energy. Food quality and safety have attributes
that play a fundamental role in choosing and accepting food products of the consumers, e.g., the
nutritional added value and the sensory attribute. Sensory quality combines some specifications
such as color, size, smell, shape, and taste, which can be evaluated by the senses of people [6].
People have a growth of interest in health awareness of consumption of probiotics in fermented
foods such as yoghurt with probiotics. At the same time, dairy producers have a lot of the focus
on consumer taste prediction for a new dairy product. [3, 18].
In the past, food sensory quality assessment was developed as one of the general techniques of
sensory study. Moreover, sensory assessment was widely used in quality evaluation, product
design in food companies to predict consumers liking of a new food product. Traditional methods
like Principal Component Analysis [7] were used to analyze sensory data of food that is supplied
by experts. This method can effeciently solve for some datasets, however sometimes it can lead
to the loss of information. In this case, other methods were applied such as random forest, neural
network, fuzzy logic and support vector machine to deal with uncertainty of sensory assessment.
For example, [8] used random forest, [9] applied neural network, [10, 11] fuzzy logic was
implemented for vital food products.
2. Machine Learning and Applications: An International Journal (MLAIJ) Vol.10, No.1, March 2023
2
Recently, some researches have approached data mining techniques for assessing the sensory
quality of food products based on their physical and chemical composition. Cortez et al. [12]
proposed a data mining method to predict the taste of white and red wines using support vector
machines, multiple regression and neural network. The results show that the support vector
machines method gives the highest accuracy. Debska et al. [13] proposed to apply the neural
network to classify Poland’s beer on the base of its chemical characteristics. Ghasemi-
Varnamkhasti et al. [14] proposed to apply neural network model to assess the sensory properties
of commercial non-alcoholic beer brands, and the result shows accuracy about 97%. Dong et al.
[15] studied the application of partial least squares, genetic algorithm back-propagation neural
network, and support vector machines to identify the relationship between flavor and sensory
evaluation of beer. The results show that support vector machines gives the best accuracy (94%).
Bi et al. [5] proposed a deep learning model based on autoencoder to extract yogurt features from
the sensory attributes and these taken features are regressed with support vector machines
analysis.
The previous researchers have demonstrated the importance of accessing data mining techniques
in sensory evaluation and quality control of food products. However, the combination between
data from sensory attributes and data from food’s images was not enough investigated. Also, the
choice of the feature extraction method that reduces the number of attributes and improves the
accuracy of the classification for the sensory evaluation model was not tested and decided in
previous researches.
In this paper, we combine two types of data to assess food quality: the sensory attributes and the
color and texture features extracted from images of yogurt samples supplemented with some of
probiotics and yeast strains.
The rest of the paper is organized as follows: Section 2 presents the data preparation. Section 3
presents the methodology. Section 4 presents the experimental results and gives a discussion on
them. Section 5 concludes the work in the paper and suggests the future work.
2. MATERIALS PREPARATION AND PANELLISTS
Microbial strain collection: All the bacterial strains, yeasts, and were kindly obtained from
microbiological resource centers (Cairo MIRCEN, Egypt).
2.1. Preparation of Probiotic Bacterial Strains
Each probiotic bacterial strain (Bifidobacterium bifidum DSM 20082, Lactobacillus Plantarum
DSM 20174 and Lactobacillus acidophilus DSM 20079) was cultivated in De-Man-Rogosa-
Sharpe broth (MRS). Then the bacterial suspension was adjusted in concentration 109
CFU/ml by
using a spectrophotometer according to [4].
2.2. Preparation of nonviable yeast Strains
Each yeast strain (Kluyveromyces lactis CBS2359 and Saccharomyces cerevisiae ATCC 64712)
was cultivated individually in Yeast Peptone Dextrose (YPD). Each yeast strain was used as
nonviable strains by heating 10 min in an autoclave in starting concentration (109
CFU/ml)
according to [4].
3. Machine Learning and Applications: An International Journal (MLAIJ) Vol.10, No.1, March 2023
3
2.3. Yogurt sample Preparation
The yogurt was made from the total fat from the milk obtained at the dairy supermarket and then
boiled for 20 minutes. In addition, that milk kept calling at 43ºC before adding yoghurt starter
and the different type of M.Os treatment into the milk.
The 36 yogurt samples were prepared and divided into 6 groups according to the type of
treatment M.Os (group 1 was Bifidobacterium bifidum, group 2 was Lactobacillus Plantarum,
group 3 was Lactobacillus acidophilus, group 4 was Kluyveromyces lactis, group 5 was
Saccharomyces cerevisiae and group 6 was a mix of all previous bacterial and yeast strains).
Each group was stored under different conditions (different storage temperatures and storage
times). The storage temperatures included low temperature (4ºC), room temperature (25ºC), and
high temperature (38ºC). However, storage periods were classified in one day and two days.
2.4. Sensory Evaluation of yogurt sample
The panel persons of sensory evaluation included 80 members (40 in two days) from the Food
Technology Department, and other departments, Arid Lands Cultivation Research Institute and
Informatics Research Institute, City of Scientific Research and Technological Applications
(SRTA-City).
The 36 yoghurt samples were detected for appearance, consistency, tenderness, flavor and overall
acceptance according to scores from 1-7 where as 1= Very poor, 2= Poor, 3= Fair, 4=Medium,
5=Good, 6= Very good and 7= Excellent was the best score according to [4].
Figure 1 shows the preparation of the samples performed in labs
Figure 1. Samples Preparation
3. METHODOLOGY
3.1. Data Pre-processing
3.1.1. Color extraction
The images of the 18 samples were acquired in RGB coloured format and then converted to
grayscale images. For each channel of the RGB images and the grayscale image, we calculated
the mean value of all values ranging from 0 to 255 in the image, also the minimum value across
4. Machine Learning and Applications: An International Journal (MLAIJ) Vol.10, No.1, March 2023
4
the image and the standard deviation. In other words, three color features were calculated for
each of the four images which gives 12 different values for color images for 18 images. Finally,
we cluster the 18 samples by means of color features in 3 clusters using k-means clustering
algorithm.
3.1.2.Texture extraction
The texture features of the 18 samples were extracted using LBP (local binary pattern). Local
Binary Pattern (LBP) is a method that used to describe texture characteristics of the surfaces. By
applying LBP, texture pattern probability can be summarised into a histogram. The 256 texture
features resulted for each of the 18 samples were clustered in 3 clusters using k-means clustering.
3.2. Feature Selection Methods
Feature selection aims to reduce the number of features in a dataset by creating new features from
the existing ones (and then discarding the original features). These new reduced set of features
should then be able to summarize most of the information contained in the original set of features
which are in our case four sensory attributes appearance, consistency, tenderness, flavour and two
attributes extracted from the images describing color and texture
3.2.1.Principal Component Analysis
Principle Component Analysis (PCA) [1] is an unsupervised learning algorithm used as a
common feature extraction method in data science. Technically, PCA finds the eigenvectors of a
covariance matrix with the highest eigenvalues and then uses those to project the data into a new
subspace of equal or less dimensions. PCA is able to do this by maximizing variances and
minimizing the reconstruction error by looking at pair wised distances. In PCA, our original data
is projected into a set of orthogonal axes and each of the axes gets ranked in order of importance.
3.2.2.Independent Component Analysis
ICA is an unsupervised learning algorithm used for linear dimensionality reduction method. It
takes as input data a mixture of independent components and it aims to correctly identify each of
them (deleting all the unnecessary noise). Two input features can be considered independent if
both their linear and not linear dependance is equal to zero [2].
3.2.3.Linear Discriminant Analysis
LDA is a supervised learning dimensionality reduction technique and Machine Learning
classifier. LDA aims to maximize the distance between the mean of each class and minimize the
spreading within the class itself. LDA uses therefore within classes and between classes as
measures. This is a good choice because maximizing the distance between the means of each
class when projecting the data in a lower-dimensional space can lead to better classification
results.
3.2.4.T-distributed Stochastic Neighbour Embedding
T-SNE is a non-linear dimensionality reduction technique, which is typically used to visualize
high dimensional datasets. T-SNE works by minimizing the divergence between a distribution
constituted by the pairwise probability similarities of the input features in the original high
5. Machine Learning and Applications: An International Journal (MLAIJ) Vol.10, No.1, March 2023
5
dimensional space and its equivalent in the reduced low dimensional space. T-SNE makes then
use of the Kullback-Leiber (KL) divergence in order to measure the dissimilarity of the two
different distributions. The KL divergence is then minimized using gradient descent.
3.3. Classification using Random Forest
Leo Breiman introduced the random forests in 2001[17]. The method builds a forest of
uncorrelated trees using a CART like procedure. Random forests average multiple deep decision
trees with the aim of reducing the variance [16].
In our work, we adopt the random forest to classify the sensory features into the seven grades
given by the expert to assess the yogurt’s quality.
3.4. The Evaluation Method
The framework of the sensory evaluation process is given by Figure 2
Figure 2. The sensory evaluation framework
4. EXPERIMENTAL RESULTS AND DISCUSSION
4.1. Quality Metrics
The Confusion Matrix (CM) gives a comparison between the actual and predicted values to know
the performance of a Machine learning classification. The accuracy (ACC) is calculated as the
number of all correct predictions divided by the total number of the dataset, i.e. the sum of the
diagonal numbers in a confusion matrix divided by all numbers in the matrix
Images
Sensory
Attributes
Color
Texture
Feature Extraction
Clustering
Combine
Features
Feature
Selection
Classification
6. Machine Learning and Applications: An International Journal (MLAIJ) Vol.10, No.1, March 2023
6
4.2. Results
In this section, we present the results of classification by comparing four feature selection
techniques and the non-use of feature selection techniques. Table 1 shows the accuracy of
classification without feature selection (FS) and with the use of the Principal Component
Analysis (PCA) as feature selection technique and the Independent Component Analysis (ICA),
the Linear Discriminant Analysis (LDA) and finally T-SNE
Table 1. The accuracy of classification with different types of feature selection
FS type Without FS PCA ICA LDA T-SNE
Accuracy 0.56 0.54 0.53 0.58 0.52
Table 2.The confusion matrix of classification without FS
0 0 1 1 1 0 0
0 4 2 0 1 0 0
0 0 12 4 1 2 2
0 1 2 18 22 7 0
0 0 1 10 54 41 3
0 0 0 3 29 98 25
0 0 0 2 3 27 55
Table 3.The confusion matrix of classification with LDA
0 1 0 1 1 0 0
1 5 1 0 0 0 0
0 0 12 5 1 2 1
0 0 3 21 17 9 0
0 0 1 10 57 39 2
0 0 0 8 24 97 26
0 0 1 0 6 23 57
From Tables 1, 2 and 3, we can notice that using a supervised ML feature selection technique
such as Linear Discriminant Analysis can improve the accuracy of the classification from 56%
without feature selection to 58%. Also, the accuracy of the classification is not high and this
opens the area of research and raises questions like: Are those sensory attributes sufficient to
assess the product? Can the grading from 1 to 7 determine well the sense of human?
In sum, we can remark the importance of feature selection to improve the accuracy of
classification and also the importance of the use of image features (color and texture) to define
the sense of the product in a precise manner.
5. CONCLUSIONS AND FUTURE WORK
In this paper, we presented a new framework to evaluate and predict the consumers’ preferences
for a dairy product (Yogurt) using sensory attributes and image analysis techniques. We select
features by different feature selection techniques. We remark that the classification after LDA
gets higher accuracy than all the other techniques. The supervised ML technique overcomes the
other traditional techniques.
7. Machine Learning and Applications: An International Journal (MLAIJ) Vol.10, No.1, March 2023
7
Future work will include the random forest regression techniques instead of classification.
Acquiring data about taste, flavour, smell from persons seems to be easier when taking a
percentage rather than a score from 1 to 7.
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