Near-infrared (NIR) spectroscopy has been widely accepted for use in the food and agricultural areas, beginning with the work of Karl Norris at the USDA to develop quality methods for agricultural products.
NIR analysers are now available for use in all aspects of food production; right from ‘farm to factory.’ Australian company Next Instruments specialises in designing and manufacturing NIR analysers for use by farmers, grain traders, grain processors and food manufacturers. The challenge has been to design instrumentation that is powerful yet simple to operate and maintain.
Reducing Energy Cost Through Boiler Efficiencyjoinerhab
Please download this PDF file for the whole content:
http://www.ncsu.edu/project/feedmill/pdf/E_Reducing%20Energy%20Cost%20Through%20Boiler%20Efficiency.pdf
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
The latest generation of near-infrared systems for online measurements in grain, flour and semolina open up new possibilities regarding gluten, water absorption and starch damage. These allow millers to optimise flour production directly and individually.
NIR analysers are now available for use in all aspects of food production; right from ‘farm to factory.’ Australian company Next Instruments specialises in designing and manufacturing NIR analysers for use by farmers, grain traders, grain processors and food manufacturers. The challenge has been to design instrumentation that is powerful yet simple to operate and maintain.
Reducing Energy Cost Through Boiler Efficiencyjoinerhab
Please download this PDF file for the whole content:
http://www.ncsu.edu/project/feedmill/pdf/E_Reducing%20Energy%20Cost%20Through%20Boiler%20Efficiency.pdf
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
The latest generation of near-infrared systems for online measurements in grain, flour and semolina open up new possibilities regarding gluten, water absorption and starch damage. These allow millers to optimise flour production directly and individually.
With a unique technology platform established over decades of work within the grain industry, FOSS can offer the most comprehensive and forward-thinking analytical solutions to help you improve your grain and milling operations. From harvest to finished products, FOSS offers solutions for improved quality control.
NIR Multi Online Technology: Real-time analysis for early detection of grain ...Milling and Grain magazine
In the grain processing industry, fluctuations in the quality of the raw materials are a given. The earlier the fluctuations are determined, the better the chances are that adjustments and the associated costs, can be kept low.
Taking NIR beyond feedstuffs - analysis to enhance pork production profitabilityMilling and Grain magazine
Swine production has been facing substantial economic challenges in recent years, due to poor crop yields and increased competition for raw materials from the biofuel industry. As a consequence, feed prices have been variable and more industrial by-products have become available. At the same time, we have experienced increasing sustainability demands on animal production, for example to reduce nutrient release in effluent, while producing more and cheaper food for an increasing world population. All this has driven the swine industry to implement more professional, accurate and precise practises.
This study aims to show that a simplified surface fitting model used for determining the energy consumption during pasteurizing milk by Pasteurizer in a dairy plant.
Application of Crop Analytics for Product Development and Business Enhancemen...Sridhar Rudravarapu
This presentation emphasies the role of modern and advanced crop analytics for product development and business enhancement with a specific focus to implement it in additional areas of application into the Indian ambit. Key suggestions have been made on effective application of crop analytics with appropriate user friendly technology/tools for implementation by the industry and government in the agriculture and food sectors to realize the benefits of product quality enhancement, premium/remunerative pricing and quick decision making to the farmer/producer in the competitive markets.
Storing and manufacturing food materials like grain can be extremely challenging. Even the slightest bit of extra humidity can create mould growth, causing the grain to deteriorate. This leads to economic losses for manufacturers in the commercial food industry.
Grains are a group of foods that includes maize, oats, barley, wheat, rye, sorghum and others. As widely reported, grain products are divided into two categories: refined and whole grain. The earlier is achieved by food manufacturers through milling, whereby the germ, bran and the endosperm are removed. The latter is just the whole grain itself. Grain milling is the milling of flour and rice; the malting of grain (primarily barley); and the mixing of prepared flour mixes and dough. Maize, rice and wheat constituted 87 percent of all grain production worldwide and 43 percent of food calories in 2003.
Development of Gastroretentive Floating Tablets Quetiapine Fumarateijtsrd
The idea of the study is to prepare and characterize a sustain release floating tablets of Quetiapine Fumarate for Schizophrenia. Materials which are used in making of effervescent Tablets are hydroxy methylcellulose HPMC. For the buoyancy sodium bicarbonate is used. Initially for the selection of formulation Definitive screening design is used which allows to study the effect of large number of factors in relatively small experiment. The optimized formulation is tested for release rate, buoyancy, hardness, thickness, floating time, swelling study and release rate. These studies shows that optimized tablet remains in stomach for 24h and shows release rate of 91 which is very desirable. Priyanka Lekhwar | Dr. P. K. Sahoo | Ravindra Agarwal | Amit Sharma ""Development of Gastroretentive Floating Tablets Quetiapine Fumarate"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd24051.pdf
Paper URL: https://www.ijtsrd.com/medicine/other/24051/development-of-gastroretentive-floating-tablets-quetiapine-fumarate/priyanka-lekhwar
Dr. Jorge Garrido - Cost Effective Influenza Sampling Strategies for PigsJohn Blue
Cost Effective Influenza Sampling Strategies for Pigs - Dr. Jorge Garrido, from the 2017 Allen D. Leman Swine Conference, September 16-19, 2017, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2017-leman-swine-conference-material
Dr. Jorge Garrido - Cost Effective Influenza Sampling Strategies for PigsJohn Blue
Cost Effective Influenza Sampling Strategies for Pigs - Dr. Jorge Garrido, from the 2017 Allen D. Leman Swine Conference, September 16-19, 2017, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2017-leman-swine-conference-material
Analysis of Micronutrients in Fortified Breakfast Cereal by Flame Atomic Abso...PerkinElmer, Inc.
This application note demonstrates the ability to accurately measure nutritional elements in breakfast cereals by flame atomic absorption using FAST Flame sample automation for increased sample throughput.
For 2016/17 (July to June), Post/New total Mexican wheat production is forecast to increase to 3.9 million metric tons (MMT). This increase of approximately 3.7 percent assumes favourable weather conditions and normal yields in the key wheat areas of Northwest Mexico (Baja California and Sonora) for the 2015/16 autumn/winter crop cycle.
With a unique technology platform established over decades of work within the grain industry, FOSS can offer the most comprehensive and forward-thinking analytical solutions to help you improve your grain and milling operations. From harvest to finished products, FOSS offers solutions for improved quality control.
NIR Multi Online Technology: Real-time analysis for early detection of grain ...Milling and Grain magazine
In the grain processing industry, fluctuations in the quality of the raw materials are a given. The earlier the fluctuations are determined, the better the chances are that adjustments and the associated costs, can be kept low.
Taking NIR beyond feedstuffs - analysis to enhance pork production profitabilityMilling and Grain magazine
Swine production has been facing substantial economic challenges in recent years, due to poor crop yields and increased competition for raw materials from the biofuel industry. As a consequence, feed prices have been variable and more industrial by-products have become available. At the same time, we have experienced increasing sustainability demands on animal production, for example to reduce nutrient release in effluent, while producing more and cheaper food for an increasing world population. All this has driven the swine industry to implement more professional, accurate and precise practises.
This study aims to show that a simplified surface fitting model used for determining the energy consumption during pasteurizing milk by Pasteurizer in a dairy plant.
Application of Crop Analytics for Product Development and Business Enhancemen...Sridhar Rudravarapu
This presentation emphasies the role of modern and advanced crop analytics for product development and business enhancement with a specific focus to implement it in additional areas of application into the Indian ambit. Key suggestions have been made on effective application of crop analytics with appropriate user friendly technology/tools for implementation by the industry and government in the agriculture and food sectors to realize the benefits of product quality enhancement, premium/remunerative pricing and quick decision making to the farmer/producer in the competitive markets.
Storing and manufacturing food materials like grain can be extremely challenging. Even the slightest bit of extra humidity can create mould growth, causing the grain to deteriorate. This leads to economic losses for manufacturers in the commercial food industry.
Grains are a group of foods that includes maize, oats, barley, wheat, rye, sorghum and others. As widely reported, grain products are divided into two categories: refined and whole grain. The earlier is achieved by food manufacturers through milling, whereby the germ, bran and the endosperm are removed. The latter is just the whole grain itself. Grain milling is the milling of flour and rice; the malting of grain (primarily barley); and the mixing of prepared flour mixes and dough. Maize, rice and wheat constituted 87 percent of all grain production worldwide and 43 percent of food calories in 2003.
Development of Gastroretentive Floating Tablets Quetiapine Fumarateijtsrd
The idea of the study is to prepare and characterize a sustain release floating tablets of Quetiapine Fumarate for Schizophrenia. Materials which are used in making of effervescent Tablets are hydroxy methylcellulose HPMC. For the buoyancy sodium bicarbonate is used. Initially for the selection of formulation Definitive screening design is used which allows to study the effect of large number of factors in relatively small experiment. The optimized formulation is tested for release rate, buoyancy, hardness, thickness, floating time, swelling study and release rate. These studies shows that optimized tablet remains in stomach for 24h and shows release rate of 91 which is very desirable. Priyanka Lekhwar | Dr. P. K. Sahoo | Ravindra Agarwal | Amit Sharma ""Development of Gastroretentive Floating Tablets Quetiapine Fumarate"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd24051.pdf
Paper URL: https://www.ijtsrd.com/medicine/other/24051/development-of-gastroretentive-floating-tablets-quetiapine-fumarate/priyanka-lekhwar
Dr. Jorge Garrido - Cost Effective Influenza Sampling Strategies for PigsJohn Blue
Cost Effective Influenza Sampling Strategies for Pigs - Dr. Jorge Garrido, from the 2017 Allen D. Leman Swine Conference, September 16-19, 2017, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2017-leman-swine-conference-material
Dr. Jorge Garrido - Cost Effective Influenza Sampling Strategies for PigsJohn Blue
Cost Effective Influenza Sampling Strategies for Pigs - Dr. Jorge Garrido, from the 2017 Allen D. Leman Swine Conference, September 16-19, 2017, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2017-leman-swine-conference-material
Analysis of Micronutrients in Fortified Breakfast Cereal by Flame Atomic Abso...PerkinElmer, Inc.
This application note demonstrates the ability to accurately measure nutritional elements in breakfast cereals by flame atomic absorption using FAST Flame sample automation for increased sample throughput.
For 2016/17 (July to June), Post/New total Mexican wheat production is forecast to increase to 3.9 million metric tons (MMT). This increase of approximately 3.7 percent assumes favourable weather conditions and normal yields in the key wheat areas of Northwest Mexico (Baja California and Sonora) for the 2015/16 autumn/winter crop cycle.
The long-awaited Panama Canal expansion opened earlier this Summer with a ceremonial ship passing through the waterway. Based on extensive research including more than 100 studies on the economic feasibility, market demand, environmental impact and other technical engineering aspects, the Panama Canal expansion involved the construction of a ‘Third Set of Locks’ that will now allow larger ships to pass through the famous canal.
Family-owned Catalyst, formerly Pharm-Tech, custom formulates and manufactures feed and nutritional supplements for customers in the livestock, poultry, pet, wildlife and aquaculture industries. It operates five production plants, three in Idaho and two in Iowa. Its range of over 100 products includes digestive aids, mineral supplements and most recently Certified Organic blends and finished feeds.
Wholegrain Ingredient Producers EDME, based in England, has pioneered an innovative new category of ingredients. Michael Carr, Sales and Marketing Director of natural ingredient producer at EDME says, “We’ve identified a growing interest in sprouted foods and have developed a brand new product category to help bakers and food manufacturers meet that interest and demand.” Sprouted grains meet the demand for new wholegrain ingredients that are nutritious, soft and tender, as well as being more palatable and digestible.
Last month, we outlined the new regulations that grain processors needed to be aware of. This included the new NFPA 652 and OSHA initiatives. This month we delve into the array of options available to control combustible dust. Grain processors need to be aware of the strengths and weaknesses of each before choosing the smartest approach.
Operating in 140 countries and boasting 90 service stations worldwide, Bühler has been at the vanguard of industrial process technologies and solutions for over 150 years. Contributing significantly to feeding the world’s ever-growing population, Bühler manufactures equipment for processing of maize, wheat, chocolate, rice, pasta and breakfast cereals globally. In fact, 65 percent of wheat milled around the globe is processed on Bühler grain mills and around 30 percent of global rice production is processed using Bühler equipment.
A consolidation of highly respected British brands E R & F Turner, Christy & Norris and Miracle Mills, Christy Turner Ltd is renowned for quality British engineering and innovation in the milling industry. With flaking mills operational around the globe, the leading UK engineering firm talks us through their top tips for increasing the longevity and performance of your flaking rolls.
On the 26 October this year, Milling and Grain magazine attended OCRIM’s 6th technical conference “Wheat, Flour and…” at its headquarters located in Cremona, home to violin extraordinaire Antonio Stradivari and arguably one of Northern Italy’s most picturesque historical cities. The annual event was aimed at clients, local residents, and friends in the worlds of industry, academia and politics.
CROP farmers anxiously watching prices fall to ever less remunerative levels have had further unwelcome news over the past couple of months from yet higher cereal and oilseed crop estimates across the Northern Hemisphere.
Joordens Zaden in Kessel, The Netherlands is an international specialist in the development and production of seed for green manure crops, forage crops and forage grasses. The seeds comply with the high quality requirements of ISTA and are strictly checked every week by external quality controllers from the Dutch General Inspection Service (NAK).
A leading miller since the company was founded in 1919, over the years Grand Moulins in Paris has been able to diversify its activities and innovate to maximize customer satisfaction.
With over a century of experience in the design, quality and installation of grain storage systems, Bentall Rowlands Storage Systems Limited is a leading UK manufacturer in complete storage and processing equipment for the agricultural and industrial markets.
Many in the milling and grain sector may be unaware that there is a significant new revenue stream available to progressive and forward-thinking mills. It focuses on the use of energy, and how by turning the power down for a relatively short period each year in line with National Grid’s and EirGrid’s requirements, companies can enjoy considerable and long-term financial rewards.
Calysta, the company developing and introducing a new protein source based on single-cell organisms - a bacterium called methylococcus – and destined for inclusion in fishfeeds, has built a ‘market introduction facility’ in Teesside, England, with production beginning in this last quarter of 2016.
Changes in flour quality are and will continue to be a problem for the bakery industry. Large amounts of grain are processed by the milling industry and many resources used to secure the flour produced have a consistent quality.
The Bakery Innovation Center (BIC) at the Bühler headquarters is now five years old. As a center for vocational training and further education for bakers and millers, it is very popular.
As “enlightened” as such statement by what Stanford University calls “the most influential English speaking philosopher of the 19th century” is, one could easily make an argument that when it comes to commodity market analysis the statement seems to be as useful as a bicycle to a fish.
Hamdard Laboratories (India), is a Unani pharmaceutical company in India (following the independence of India from Britain, "Hamdard" Unani branches were established in Bangladesh (erstwhile East Pakistan) and Pakistan). It was established in 1906 by Hakeem Hafiz Abdul Majeed in Delhi, and became
a waqf (non-profitable trust) in 1948. It is associated with Hamdard Foundation, a charitable educational trust.
Hamdard' is a compound word derived from Persian, which combines the words 'hum' (used in the sense of 'companion') and 'dard' (meaning 'pain'). 'Hamdard' thus means 'a companion in pain' and 'sympathizer in suffering'.
The goals of Hamdard were lofty; easing the suffering of the sick with healing herbs. With a simple tenet that no one has ever become poor by giving, Hakeem Abdul Majeed let the whole world find compassion in him.
They had always maintained that working in old, traditional ways would not be entirely fruitful. A broader outlook was essential for a continued and meaningful existence. their effective team at Hamdard helped the system gain its pride of place and thus they made an entry into an expansive world of discovery and research.
Hamdard Laboratories was founded in 1906 in Delhi by Hakeem Hafiz Abdul Majeed and Ansarullah Tabani, a Unani practitioner. The name Hamdard means "companion in suffering" in Urdu language.(itself borrowed from Persian) Hakim Hafiz Abdul Majeed was born in Pilibhit City UP, India in 1883 to Sheikh Rahim Bakhsh. He is said to have learnt the complete Quran Sharif by heart. He also studied the origin of Urdu and Persian languages. Subsequently, he acquired the highest degree in the unani system of medicine.
Hakim Hafiz Abdul Majeed got in touch with Hakim Zamal Khan, who had a keen interest in herbs and was famous for identifying medicinal plants. Having consulted with his wife, Abdul Majeed set up a herbal shop at Hauz Qazi in Delhi in 1906 and started to produce herbal medicine there. In 1920 the small herbal shop turned into a full-fledged production house.
Hamdard Foundation was created in 1964 to disburse the profits of the company to promote the interests of the society. All the profits of the company go to the foundation.
After Abdul Majeed's death, his son Hakeem Abdul Hameed took over the administration of Hamdard Laboratories at the age of fourteen.
Even with humble beginnings, the goals of Hamdard were lofty; easing the suffering of the sick with healing herbs. With a simple tenet that no one has ever become poor by giving, Hakeem Abdul Majeed let the whole world find compassion in him. Unfortunately, he passed away quite early but his wife, Rabia Begum, with the support of her son, Hakeem Abdul Hameed, not only kept the institution in existence but also expanded it. As he grew up, Hakeem Abdul Hameed took on all responsibilities. After helping with his younger brother's upbringing and education, he included him in running the institution. Both brothers Hakeem Abdul Hameed and Hakim Mohammed
Vietnam Mushroom Market Growth, Demand and Challenges of the Key Industry Pla...IMARC Group
The Vietnam mushroom market size is projected to exhibit a growth rate (CAGR) of 6.52% during 2024-2032.
More Info:- https://www.imarcgroup.com/vietnam-mushroom-market
Hotel management involves overseeing all aspects of a hotel's operations to ensure smooth functioning and exceptional guest experiences. This multifaceted role includes tasks such as managing staff, handling reservations, maintaining facilities, overseeing finances, and implementing marketing strategies to attract guests. Effective hotel management requires strong leadership, communication, organizational, and problem-solving skills to navigate the complexities of the hospitality industry and ensure guest satisfaction while maximizing profitability.
3. Near-infrared (NIR) spectroscopy
has been widely accepted for use
in the food and agricultural areas,
beginning with the work of Karl Norris at
the USDA to develop quality methods for
agricultural products.
However, since NIR spectra are over-tones
and combinations of fundamental IR
spectra, the peaks tend to lose definition and
broaden, representing general features due
to CH, NH and OH stretch and bend fre-quencies,
as contrasted to their fundamental
frequencies that define IR.
Thus, it was not until mathematical
and computer modeling of NIR spectros-copy
was utilised, that information could be
obtained from NIR. With the implementa-tion
of chemometrics methods, a valuable
tool for differentiation and quantification of
agricultural and food components became
available.
Coupled with the reduced requirement
for sample preparation, one of the major
strengths of NIR and the implementation of
NIR in a portable, handheld unit such as the
Thermo ScientificTM microPHAZIRTM ana-lyzer,
NIR spectroscopy and the subsequent
identification and quantification of food,
feed and agricultural samples can be taken
from the laboratory directly to the field or
warehouse.
NIR spectroscopy is well recognized as a
reliable instrument to predict moisture, pro-tein
and fat in food or agricultural samples.
One of the most common uses – dating
from the earliest implementation of NIR
spectroscopy – is the quantification of pro-tein,
moisture and ash in flour1,2.
Study Scope
In the following discussion we will pre-sent
the progress of this study from the
preliminary assessment of a small subset of
the library to the resulting predictive models.
Initially models were built in-house to test
a subset of wheat and soya samples, and to
develop the proof of concept. Based on the
Collaboration with Aunir allowed Thermo Scientific to utilise this
well-established and robust INGOTTM3 library, developed over
several years, in utilising its handheld portable NIR instruments.
Today these predictive models can be moved from the
confines of a lab-based environment into the field, thus opening
the availability of this widely recognised and useful method.
success of these models for prediction of
common physical parameters, a collabora-tive
study was done with Aunir, using its
INGOT library and the microPHAZIR ana-lyzer.
(Appendix A INGOT Library)
In this latter study, Aunir supplied 14
models for feed and feed ingredients based
on the INGOT calibration library. In col-laboration
with Aunir these models were
validated, refined and using in-house meth-ods,
calibration transfer and bias
correction were performed.
Instrumentation
All testing was done using
the portable microPHAZIR AG
analysers, and with a specially
designed sample cup for food,
feed and agriculture (FFA) appli-cation.
The sample cup can be
attached to the instrument and
manually rotated to multiple posi-tions.
The sampling window on
the instrument is designed to
locate at an off-the-center posi-tion
such that each rotation of
sample cup results in a differ-ent
portion of the sample being
presented to the instrument. The
samples were scooped and sam-pled
at given replicate times and
the predicted results were aver-aged
as indicated.
Materials
All materials tested on the
microPHAZIR AG analyzer units
were used as received from Aunir.
These were ground samples that
covered the range of properties
appropriate to the model. These
materials were used to test the
capability of the microPHAZIR
analyser for quantitative analysis
of FFA parameters.
In total 11 parameters were
the most common constituents
of interest in FFA applications and they are
listed in Table 1.
For actual applications, not every param-eter
could be predicted for each model.
Model parameters are restricted to the
most useful and practical for quality evalu-ation
relevant to certain ingredient or feed
sample type. Of particular relevance to most
applications is the prediction of protein,
moisture and fat (oils).
NIR
IN THE FIELD,
FACTORY &
WAREHOUSE
by S.K. Schreyer, Michelle
Pressler and Lin Zhang of
Thermo Fisher Scientific,
Tewksbury, MA, United
States
Table 1 Most commonly predicted parameters for FFA
applications
Predicted
Description
Parameter
Moisture - -
Oil A Fat (EE) ether extract
Oil B Fat (AH) acid hydrolysis
Protein - -
Fibre crude fibre
Ash - inorganic matter
Starch - enzymatic starch
Sugar Reducing sugar -
NCGD neutral cellulose plus
gamanase -
NDF neutral detergent fibre hemicellulose + ADF
ADF acid detergent fibre cellulose, lignin,
fibre-bound N
Table 2: Reference values for wheat
Wheat Moisture Oil A Oil B Protein Starch Sugar
Mean 13.14 1.29 2.32 12.69 57.82 3.49
Range 2.12 0.51 0.77 5.90 8.59 2.96
Minimum 12.39 0.94 1.77 10.01 54.48 1.52
Maximum 14.51 1.45 2.54 15.91 63.07 4.48
Table 3: Reference values for soy
Soya Moisture Oil A Oil B Protein Starch Sugar
Mean 2.44 1.67 2.37 49.84 4.84 10.01
Range 2.44 1.89 1.83 6.19 1.83 4.50
Minimum 10.44 0.70 1.46 45.69 4.03 7.95
Maximum 12.88 2.59 3.29 51.88 5.86 12.45
F
4. GRAIN&FEED MILLING TECHNOLOGY September - October 2014 | 19
Results and Discussion
Part 1: Sample preparation and
spectra collection
In the initial testing phase the appli-cability
of the unit to predict moisture,
protein and fat was evaluated using an ini-tial
set of materials received from Aunir.
This set consisted of 20 samples each of
ground wheat, soy and corn, covering an
appropriate range of parameters.
The parameters evaluated for wheat
and soy are indicated in Table 2 and
Table 3. For initial testing wheat was
used as an indication of the performance
of cereals (Aunir Group 10) and soy was
used as indication of the performance of
high protein-low oil (Aunir group 30)
All samples were used as received
(ground) and placed in the quartz sample
cup for NIR spectra collection. Spectra
were collected on microPHAZIR AG
analysers, each over a wavelength range
from 1595-2395nm, in diffuse reflectance
mode.
Spectra were collected over six posi-tions
of the sample cup in order to
compensate for sample inhomogeneity.
In total, this resulted in six spectra col-lected
per sample and each sample was
also tested three times, with replace-ment,
resulting in 24 spectra collected
for each sample. This sampling process
was repeated for each of the 20 samples.
Samples were scanned in a randomised
manner to compensate for any sampling
correlations.
The spectral data were then evalu-ated
and quantitative individual PLS-1 mod-els
were constructed using our internal
chemometrics software package Thermo
Method GeneratorTM software (TMG). This
software was developed for use with the
microPHAZIR analyser. An example of the
spectra collected on each microPHAZIR
analyser is shown in Figure 1.
Aside from baseline offset, all spectral
features were similar across the different
microPHAZIR analysers, with no obvi-ous
spectral non-conformities. Based on
one microPHAZIR analyzer, the resulting
spectra collected from the 20 wheat
samples are shown in Figure 2.
Figure 1: Example of wheat spectra
from 4 different instruments
Figure 2: Spectra of wheat across the
full range for protein reference values
Figure 3: Preprocessed spectra of
wheat samples
Figure 4: Correlation plot of the
reference and the predicted values for
wheat protein
Excellent firms don’t believe in excellence
- only in constant improvement and constant change.
Turning ideas into opportunities.
PROGRESSIVE FOOD PROCESSING
—Tom Peters
Is it time to shift production to a more favorable
continuous process?
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5. F 20 | September - October 2014 GRAIN&FEED MILLING TECHNOLOGY
Table 4: Predictions for wheat samples from 4 instruments with
comparison to the reference values
Predicted results for Wheat Sample
Wheat Moisture Protein Oil A Sugar Starch
Reference 12.97 13.08 1.44 3.52 56.88
FFA 2319 12.97 12.70 1.35 3.70 58.71
FFA 2231 13.02 12.51 1.47 4.05 58.40
FFA 2045 12.81 13.04 1.32 3.72 58.87
Table 5: Predicted results for the soya samples across 4 instruments with
comparison to the reference values
Predicted results for Soya Sample
Soya Moisture Protein Oil A Oil B Sugar Starch
Reference 12.27 50.98 1.81 2.57 4.48 9.33
FFA 1 12.15 51.11 1.97 2.81 4.41 9.27
FFA 2 12.32 50.99 1.94 2.76 4.43 9.19
FFA 3 12.05 51.41 1.96 2.75 4.46 9.32
FFA 4 12.19 51.21 1.95 2.82 4.31 9.44
Table 6: Model prediction performance from 4 instruments on the 20 wheat samples to compare averaging vs not averaging
Sampling
Part 2: Model development example
(Protein)
Preprocessing was performed using
standard normal variate (SNV) to offset par-ticle
inhomogeneity and particle density and
packing differences, followed by Savitsky-
Golay smoothing (1st derivative, 7 point
smooth, 2nd order polynomial).
The effective wavelength region used
for protein determination was adjusted in
each case. For protein, the wavelength
was restricted to 1716.7-2359.6nm. These
regions include the N-H overtone and
combination bands. Results are on Figure 3.
It was determined that three factors were
optimal for the PLS model, based on the plot
of factors and associated root mean square
error (RMSE) of cross validation.
Associated loadings plots for the first
three factors also substantiate the use of
three factors in the resulting PLS model
as past three factors loadings plots show
increased loss of information and increased
noise. Factors indicate the importance of
the CH combination bands at 1700, the
overtone bands at 2200-2300nm and the
nitrogen overtone and combination bands at
2000-2200 regions.
The resulting PLS model gave a RMSE of
calibration of 0.25 percent and a RMSECV of
0.27 percent for protein prediction. The pre-dicted
results gave a R2 of 0.97, as shown in
the correlation plot for prediction of protein
across the 20 calibration samples on Figure 4.
Further refinement of the model can
be made by omitting the water peak and
restricting the wavelength regions to 1716.7-
1900,2000-2359.6nm.
Part 3: Proof of concept: Predicting
wheat and soy parameters
Similar models were built for the other
parameters for wheat (moisture, oil, sugar
and starch). Each individual model was evalu-ated
for the optimised preprocessing condi-tions
and once determined, each model was
then evaluated for the calibration accuracy.
Based on this, models were built for predic-tion
of each parameter.
These individual models were then com-bined
together into a master multi-PLS
model to be used for wheat prediction.
The preceding results and discussion
was based on the calibration data set. In
order to ascertain the functionality of the
model built, and assess the predictive abil-ity,
the wheat application was then loaded
onto four independent microPHAZIR AG
analysers.
Predictions were obtained for a series of
runs. Samples were scooped into the sample
cup and for each sample six predictions
were made; each prediction after a rotation
of the sample cup. This was repeated with
the same sample on all four units. Then new
samples were used and the above proce-dure
was repeated two more times. This
gave a total of 18 predictions per unit, and an
overall number of 54 predictions on protein.
The averaged results for prediction of associ-ated
parameters in wheat are shown below
for each of the four microPHAZIR analyser
units in Table 4.
A similar procedure was followed for soy
analysis.
The individual models were made based
on data collected from the microPHAZIR
analyser units, and evaluated individually to
optimise model accuracy. These individual
models were then combined into a soy
multi- PLS model, and loaded onto the four
units. In the case of soy, predictions were
made for moisture, protein, both types of oil,
sugar and starch.
Predictions were generated similar to the
wheat models – predictions were averaged
over six positions and three samples – and
compared to the reference values supplied
by Aunir. Prediction results are shown for
soy in Table 5.
Part 4. Evaluation of Aunir models:
Cereals
The first library and model evaluated
from Aunir was group 10 (cereals). This
comprises data from eight cereal types
including wheat and corn. This model was
built by Aunir using their internal INGOT
library of 30,000-plus samples collected on
traditional benchtop NIR instrument with
high spectral resolution and larger spectral
range than microPHAZIR analyser.
To augment the library with microPHAZ-IR
analyser data, additional samples were
collected using two parent microPHAZIR
Pattern
Constituent Unit 2319 Unit 2325 Unit 2395 Unit 2398
SEP Bias
Bias
Corrected
Error
SEP Bias
Bias
Corrected
Error
SEP Bias
Bias
Corrected
Error
SEP Bias
Bias
Corrected
Error
Averaging Moisture 0.59 -0.46 0.37 0.60 -0.44 0.41 0.43 -0.06 0.43 0.46 -0.07 0.46
Oil A 0.29 -0.24 0.17 0.57 -0.55 0.14 0.52 -0.50 0.16 0.53 -0.51 0.16
Oil B 0.41 -0.38 0.15 0.67 -0.66 0.13 0.68 -0.66 0.15 0.62 -0.60 0.16
Protein 0.31 0.12 0.28 0.29 -0.05 0.29 0.30 0.01 0.30 0.39 0.16 0.36
Fiber 0.95 -0.86 0.40 1.57 -1.51 0.42 1.24 -1.14 0.48 1.28 -1.20 0.45
Ash 0.28 -0.06 0.28 0.31 -0.18 0.25 0.25 -0.02 0.25 0.29 -0.13 0.25
Starch 1.86 0.76 1.70 3.25 2.72 1.78 2.05 0.61 1.95 2.44 1.54 1.90
No
Averaging
Moisture 0.60 -0.46 0.39 0.61 -0.44 0.43 0.45 -0.06 0.45 0.49 -0.07 0.48
Oil A 0.31 -0.24 0.20 0.58 -0.55 0.19 0.54 -0.50 0.20 0.54 -0.51 0.20
Oil B 0.42 -0.38 0.17 0.68 -0.66 0.18 0.69 -0.66 0.18 0.63 -0.60 0.19
Protein 0.36 0.12 0.34 0.32 -0.05 0.32 0.38 0.01 0.38 0.45 0.16 0.42
Fiber 0.97 -0.86 0.44 1.58 -1.51 0.46 1.25 -1.14 0.52 1.29 -1.20 0.48
Ash 0.30 -0.06 0.29 0.32 -0.18 0.27 0.26 -0.02 0.26 0.30 -0.13 0.27
Starch 1.98 0.76 1.83 3.31 2.72 1.89 2.15 0.61 2.06 2.54 1.54 2.02
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7. 22 | September - October 2014 GRAIN&FEED MILLING TECHNOLOGY
analyser instruments at Aunir. The library
from benchtop NIR was transferred to
match the microPHAZIR analyser platform
and transferred spectra were combined with
the data collected on the parent units.
From this data set, models were devel-oped
for prediction of quality parameters
on cereal samples. Twenty wheat samples
were measured on four instruments. For
each sample, a single compaction of sample
is loaded into sample cup with three evenly
spaced rotations during sampling.
Tables 6, 7 and 8 summarise the predic-tion
performance of Group 10 model on 20
wheat samples using four instruments. Three
metrics are presented in the tables, i.e., SEP,
bias and M and bias corrected error. These metrics
their performance lling
in this International
application are
explained below.
SEP is the standard error of prediction and
is an assessment Directory of overall error in predic-tion.
SEP includes both systematic error and
random error. Bias is calculated as the mean
difference between model predicted and ref-erence
values and is an estimate of systematic
error in prediction. For the FFA application,
bias could be caused by several factors.
First, there might be a difference in
wet chemistry test methods for reference
values. For example, a customer may prefer
a certain type of protein analysis which
might have a constant bias relative to the
wet chemistry used by INGOT library. This
difference is not addressed in the study but
could be easily removed by the on-board
bias and slope correction software.
The on-board software can be configured to
apply customer bias and slope to the predicted
concentration of a constituent of interest.
Second, there is always some difference
between parent and child instrument. This
difference could be a result of the different
way light propagates from the instrument
and the sample, irrespective of how tight
manufacturing control is. This difference
results in bias of the predicted parameter.
For quantitative applications of NIR, some
NIR instrument manufacturers perform
some kind of instrument standardisation to
improve prediction accuracy.
Our results show that bias across instru-ments
are negligible in the context of FFA
application.
Thus, no instrument standardisation is
performed for the current release.
Third, the calibration model is built upon
a group of similar samples (barley, corn,
wheat, rye, etc.) and here the prediction is
performed on a specific sample type (wheat).
In theory, the model presents no bias
when an imaginary “averaged” sample from
different sample groups is predicted.
However, a small systematic bias is
expected when one specific sample type is
predicted. Again, this could be addressed by
on-board bias/slope correction if needed.
Bias corrected error is calculated by remov-ing
the contribution of bias from SEP. This
metrics represents the error cause by uncer-tainty
in measurement system itself. Thus,
proper sampling could further reduce this
error.
The performance of a model across four
instruments was shown to be satisfactory.
Table 8: Comparison of bias between the 4
instruments for wheat sample prediction
max
bias
min
bias
Range of
Bias Among
instruments
Moisture -0.06 -0.46 0.40
Oil A -0.24 -0.55 0.31
Oil B -0.38 -0.66 0.28
Protein 0.16 -0.05 0.21
Fiber -0.86 -1.51 0.65
Ash -0.02 -0.18 0.16
Starch 2.72 0.61 2.11
Moisture -0.06 -0.46 0.40
Oil A -0.24 -0.55 0.31
Oil B -0.38 -0.66 0.28
Protein 0.16 -0.05 0.21
Fiber -0.86 -1.51 0.65
Ash -0.02 -0.18 0.16
Starch 2.72 0.61 2.11
For example, in the case of protein, the SEP
values range from 0.29 to 0.39 with bias
ranging from -0.05 to 0.16.
The mean SEP is 0.33 with a mean of
absolute bias of 0.08. The bias corrected
mean SEP is 0.31.
With regard to sampling error, comparing
the case of averaging versus non-averaging
(averaging over multiple positions over sam-ple
cup), the bias corrected error is reduced
0.37 to 0.31. Since this error is part of SEP,
the corresponding reduction in SEP is 0.05.
Summary
The use of a portable, handheld NIR
instrument to predict protein, moisture, fat
and other parameters on feed and agricul-tural
ingredients such as wheat and other
cereals, has been shown to give reliable and
robust results.
Results were shown as a progression from
proof of concept through to final optimised
models using protein and moisture analysis
in some detail. In either case, a robust and
useable prediction model was achieved, with
relatively low prediction errors.
The models from Aunir are very robust
and no calibration standardisation was need-ed.
Some instrument bias was observed but it
is expected that the on-board bias/offset cor-rection
software could be used to fine-tune
the predictions. Further the sampling error
could be reduced by first grinding the sample
before scanning over multiple positions.
References
1 Osborne B.G., Fearn T., 1983 Journal of Food
Technology, 18, 453-460
2 Norris, K.H., Williams, P.C., 1984 Cereal Chemistry
61(2), 158-165
3 Aunir INGOT Animal Feed Library, Towcester, UK;
info@aunir.co.uk
4 Daredenne, P., Pierna J. A. F., Vermeulen, P., Lecler,
B., Baeten, V., 2010 Applied Spectroscopy, 64(6),
644-648
Table 7: Model prediction performance based
on average of the 4 instruments. SEP and bias
results included to compare averaging across
samples and not averaging
Constituent Average across 4 units
SEP Absolute
Value
of Bias
Bias
Corrected
Error
Averaging
Moisture 0.53 0.26 0.42
Oil A 0.49 0.45 0.16
Oil B 0.60 0.58 0.15
Protein 0.33 0.08 0.31
Fiber 1.28 1.18 0.44
Ash 0.28 0.10 0.26
Starch 2.46 1.41 1.84
No Averaging
Moisture 0.54 0.26 0.44
Oil A 0.50 0.45 0.20
Oil B 0.61 0.58 0.18
Protein 0.38 0.08 0.37
Fiber 1.29 1.18 0.48
Ash 0.30 0.10 0.27
Starch 2.55 1.41 1.95
2013/14
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