Flour is an important staple food in many parts in the world and is used to make numerous types of food, such as bread, pasta, noodles, crackers, cakes, and pastries. While wheat accounts for around 85% of all global flour products, other types of flour are made such as rice, oat, corn, and potato, especially in parts of the world where wheat is not easily grown. It is a significant source of starch and carbohydrates. Cereal flour consists of the endosperm, germ, and bran together (also known as whole-grain flour) while refined flour is the endosperm alone. The flour milling process can be complex based on the desired product and proper milling and blending requires skilled millers, even in large plants. As is the case with all agricultural products, proper processing, storage, and transport are essential. The proper product and food it is best suited to make is very much dependent on the quality control parameters and nutritional composition of the desired product. For example, high gluten protein flour is best suited for making breads and results in a harder and stronger flour. Low gluten protein flour is softer and better suited for baked goods. There are different classifications of flour based on the protein content. Extraction yield measured by ash is another significant quality parameter. The use of NIR spectroscopy to determine ash content in flour is a certified AACC method. Whole-grain flour has a higher nutritional content and an extraction yield typically close to one hundred percent. Refined flour (almost always treated to make it white and subsequently fortified with nutrients) has a lower extraction yield between 50% and 70% because it only contains the endosperm. Like many foods, flour is subject to adulteration. Different species and brands can vary greatly in nutritional content and market value. Visual differentiation is especially difficult for white flours and standard methods using wet chemistry are impractical to implement, especially in small markets. While gluten-free flour products do exist, the prevalence of wheat flour which does contain gluten creates a need to develop gluten-free wheat products including flour. Genetic engineering and transgenic wheat are a subject of research for this purpose, but the practice is controversial and no gluten-free wheat flour product has been approved for commercial use. There is a need to develop fast, non-invasive testing methods to meet the evolving challenges in producing quality flour. One such method that has been examined is NIR Spectroscopy.
- Total Polyphenol Content (TPC)
- DPPH radical scavenging activity
- Resistant starch
- Digestible starch
- Total starch
- Blend content in potato and wheat flour blends
- Flour species classification
- Oat flour adulteration
- Tuber flour discriminant analysis
Summary of Published Papers, Articles, and Reference Materials
Near-Infrared Reflectance Spectroscopic Analysis of Moisture, Fat, Protein, and Physiological Activity in Buckwheat Flour for Breeding Selection
Soba noodles made from buckwheat are a popular food in Japan and are considered nutritious because of high mineral and protein content. While standard nutritional parameters such as moisture, fat, and protein have been analyzed extensively, such methods can involve expensive and time-consuming wet chemistry tests and provide insufficient data for breeding projects. Breeding projects aim to improve grain yield through plant structure improvement and the necessary analysis is far too difficult for a large number of breeding lines. NIR spectroscopy was examined as method for determining moisture, fat, and protein in both common buckwheat and tartary buckwheat as well as for the physiological activity parameters Total Polyphenol Content (TPC) and DDPH radical scavenging activity in common buckwheat flour. Common and tartary buckwheat grain samples from three consecutive harvest seasons were procured for the study. Most samples came from different areas of Japan but many other countries were included, such as Canada, China, France, Nepal, Pakistan, and Russia. Grain was milled, sieved, and stored as flour at 5°C until analysis. NIR spectra were collected from 1100 nm to 2500 nm at 2 nm intervals. After scanning, standard reference tests for moisture, fat, protein, Total Polyphenol Content, and Radical Scavenging Activity were performed on the samples. For the first three parameters, samples from the first two harvest seasons were used for the tests while the third harvest season samples were used for the physiological activity tests. Various pre-processing methods were performed on the NIR spectra before chemometric modeling. Based on initial data analysis of the NIR spectra, significant wavelengths were chosen and Multiple Linear Regression (MLR) analysis was performed on the data to correlate the moisture, fat, protein, and physiological activity parameters to the NIR spectra.
|Moisture||R2= 0.918||SEP= 0.147%|
|Fat||R2= 0.969||SEP= 0.095%|
|Protein||R2= 0.957||SEP= 0.389%|
|Total Polyphenol Content||R2= 0.938||SEP= 0.164 GA Equivalence|
|DDPH Radical Scavenging Activity||R2= 0.973||SEP= 0.915 Trolox Equivalence|
Results were excellent for moisture, fat, and protein and proved the feasibility of using NIR spectroscopy as a method for measuring these parameters in buckwheat flour. A separate validation set verified the validity of the models. The physiological activity parameters warrant closer examination. Units for both of them are expressed as values determined in the standard reference tests – mg-GA Equivalence/g for TPC and nmol-Trolox Equivalence/g for the DPPH radical scavenging activity. While the R2 correlation coefficients are high, validation predictions showed that the NIR method was suitable for measuring the component in Total Polyphenol Content but not the actual activity. However, an estimation of radical scavenging activity can be made from TPC and such an estimation can be used for simple and rapid breeding selection. Overall, this study showed that moisture, fat, and protein can be determined at a level sufficient for quality control and physiological activity can be estimated at a level sufficient for quality evaluation using NIR spectroscopy. CiNii Articles - Near-Infrared Reflectance Spectroscopic Analysis of Moisture, Fat, Protein, and Physiological Activity in Buckwheat Flour for Breeding Selection
Application of Near Infrared Transmission for the Determination of Ash in Wheat Flour
Ash is an important constituent for wheat flour quality and an indicator of flour purity. Even under optimum conditions, the milling process cannot fully separate the starchy endosperm from the bran. Ash indicates how completely and efficiently the endosperm has been separated from the bran and is defined as the mineral residue of flour determined by oven burning. While a standard and simple method, it is time-consuming and a quick method for ash determination has always been a need for the milling industry. NIR spectroscopy was examined for this purpose and using NIR spectroscopy to determine ash content in flour is a certified AARC method. One hundred and thirteen wheat flour samples from four separate mills were procured for the study. A NIR analyzer with a pre-built calibration for ash was used to scan the samples from 850 nm to 1050 nm. The calibration is designed to work for ash values from 0% to 1.10%. Using the collected spectra and calibration model, values were calculated for ash of the samples. A standard reference test was then used to determine the ash content of all samples. By definition, ash is the quantity of mineral matter which remains as an incombustible residue of the tested substance. Samples were burned to ashes at 900°C and the ash quantity was expressed to dry matter.
|Ash||R2= 0.954||SEP= 0.054%|
The correlation coefficient showed good agreement between the NIR method and traditional reference method for ash. The SEP was higher than expected but some factors were likely the cause of this. The error was very low for samples from certain mills while higher for others. When using pre-built calibrations, a bias and slope correction is often necessary when using different types of samples and this analysis was not performed in this study. It is also known that ash content better correlates to higher wavelengths in the NIR spectrum and the range of the spectrometer used in this study was limited. Other studies have shown a much lower SEP and an R2 as high as 0.99. More calibration work is necessary but this study showed the feasibility of using an NIR spectrometer and a pre-built calibration model to determine ash content in wheat flour. Application of Near Infrared Transmission for the Determination of Ash in Wheat Flour – CORE
Simultaneous Determination of Ash and Protein Contents in Wheat Flour Using NIR-PLS and DRIFT-PLS
Protein and ash are two essential quality control parameters in flour. The protein level is important for determining whether a flour is better suited for a bread or baked good product. Higher protein makes a flour harder and more binding while lower protein makes a flour softer and more refined. Ash is a good indicator of extraction yield and whole-grain starch has a much higher extraction yield than white flour because it still contains the mineral parts of the outer section of the grain. A need exists for a fast, non-invasive method for determining protein and ash content in wheat flour and NIR spectroscopy was examined for this purpose. One hundred samples of wheat flour from different mills were procured for the study. Reference tests indicated that the protein values ranged from 8.85% to 13.23% and the ash values ranged from 0.330% to 1.287%. The traditional methods of Kjeldhal for protein and gravimetric were used for ash. NIR spectra were collected using an FT-NIR spectrometer. Various pre-processing methods were applied to the spectral data before chemometric analysis. Partial Least Squares (PLS) calibration models were created using the NIR spectra and reference values for protein and ash.
|Protein||R2= 0.998||SEC= 0.33%|
|Ash||R2= 0.997||SEC= 0.07%|
The modeling results were validated using cross-validation and the predictions showed that the models could be used to accurately determine protein and ash content in wheat flour. The potential exists for NIR spectroscopy to replace traditional expensive and time-consuming methods for determining ash and protein in wheat flour. (PDF) Simultaneous determination of ash content and protein in wheat flour using infrared reflection techniques and partial least-squares regression (PLS) (researchgate.net)
Simultaneous Estimation of Amylose, Resistant, and Digestible Starch in Pea Flour by Visible and Near-Infrared Reflectance Spectroscopy
Pea is one of the major legumes in the world and production of peas has greatly increased over the last decade in the Northern Great Plains area of the United States. Much of the pea product produced in the United States is exported to Asia and while dry pea is not considered a major starch source compared to other precursors of flour, it is widely processed into noodles. After mung bean, pea starch is considered the second best source of all grain legumes for processing starch noodles. Starch consists of amylose and amylopectin and based on digestibility, pea starch can be classified into digestible starch (DS) and resistant starch (RS). RS is resistant to digestion in the small intestine and can be fermented in the colon to produce short-chain fatty acids, which may lead to health benefits. Research shows that higher amylose levels are correlated with RS. A need exists to determine RS content in flour, but traditional methods are expensive, time-consuming, and are impractical for implementing for large-scale testing. NIR spectroscopy was examined for determining starch contents in pea flour. One hundred twenty-three pea seed samples from Montana were procured for the study and ground into flour. A Vis-NIR spectrometer that scans from 200 nm to 1080 nm and 900 nm to 2300 nm was used to scan the samples. Each sample was scanned from 300 nm to 900 nm and 900 nm to 2300 nm and both wavelength ranges were combined into a single spectrum for each sample. Standard reference tests were used to determine amylose, RS, and DS for the sample. Total starch was calculated from the sum of RS and DS. Various pre-processing methods were performed on the spectral data before chemometric analysis. Partial Least Squares (PLS) calibration models were first created using the full spectral range to determine the wavelength bands of interest. The relevant wavelength ranges were then used to create Multiple Linear Regression (MLR) models, which only use specific wavelengths to correlate the spectral data to the parameters of interest.
|Total Starch||R2= 0.93|
Results for the MLR models showed good correlation and the cross-validation results were in good agreement with the reference values for the samples. Each model used between eight and thirteen different wavelengths for the correlation. This study showed the feasibility of using NIR spectroscopy to determine starch parameters in pea flour. For use in a practical setting, more calibration work and a larger sample set would likely help to improve the results. https://www.tandfonline.com/doi/pdf/10.1080/10942912.2018.1485027
Development of a Predictive Model to Determine Potato Flour Content in Potato-Wheat Blended Powders Using Near-Infrared Spectroscopy
Potato is a good source of dietary energy and micronutrients. The development of staple foods using blends of potato flour and wheat flour has become prevalent in recent years, especially in China. Because flour is a worldwide staple food, it can be subject to adulteration and there is a need for fast, non-invasive, and large-scale testing methods to both prevent adulteration and to determine quality for market regulation purposes. NIR spectroscopy is a proven method for determining quality parameters in potatoes, such as dry matter, starch, protein, and sugar. For the first time, NIR spectroscopy was examined as a method for quantifying potato flour content in blends of potato flour and wheat flour. Both potato flour and wheat flour were purchased from a local manufacturer for the study. Blends were prepared by mixing wheat flour with potato flour at 1% by weight increments from 0% to 100% wheat flour for a total of one hundred and one samples. Samples were mixed in a mixer. Some samples were split in half to use as validation samples. Samples were scanned from 890 nm to 1100 nm. Three spectra were collected per sample and averaged into one spectrum. Various pre-processing methods were applied to the spectral data before chemometric modeling. A Partial Least Squares (PLS) calibration model was created correlating the NIR spectra to the percentage of potato flour in the blends. In total, ninety-two samples were used for the calibration model and fifteen were used for independent predictions.
|Potato Flour Content||R2= 0.9995||SEP= 0.69%|
While the correlation coefficient for determining the potato flour content was high and the SEP was low, the results shown here warrant closer examination. It is possible that the calibration model is actually correlating to the changes in blend content but with only two sets of samples used, this is unclear and unproven. If the two sets of samples have any differing chemical and physical properties (such as moisture) and they are being blended together with the ratio of those properties changing, the model may very well be correlating to something else entirely besides the blend content. For further validation, this study should be conducted incorporating different sources of variability into the potato flour and wheat flour samples. Samples procured from different growing areas, different manufacturers, and different harvests would prove that the calibration model was actually determining blend content and not correlating to another property. Development of a predictive model to determine potato flour content in potato-wheat blended powders (tandfonline.com)
Quantitative Analysis of Adulterations in Oat Flour by FT-NIR Spectroscopy, Incomplete Unbalanced Randomized Block Design, and Partial Least Squares
Oat is a widely utilized food for both human consumption and industrial uses. It has high nutritional value and a characteristic flavor. Oat flour is an important food in breakfast cereals and offers an alternative to wheat flour. However, the yield when producing oat flour is less than that of wheat flour, making oat flour more expensive than wheat and some other types of flour. The appearance of oat and wheat flours is similar, making oat flour subject to adulteration with wheat flour. A need exists for a fast, non-invasive method that can be implemented on a large scale to determine adulteration of oat flour and NIR spectroscopy was examined for this purpose. Sets of oat and wheat kernels were procured from domestic markets in China, all coming from the same harvest season. In total, oat kernels came from five separate markets and wheat kernels came from seven separate markets. The kernels were dried, milled, and filtered through a sieve to make flour. Adulterated oat flour samples were created by adding increments of 5% weight of wheat flour to oat flour. Samples ranged from pure oat flour with 0% wheat flour to 50% oat flour and 50% wheat flour. In total, one hundred twenty samples were prepared for developing calibration models and another one hundred samples were prepared for model validation. NIR spectra were collected using an FT-NIR spectrometer. Samples were scanned from 12000 cm-1 to 4000 cm-1 at 4 cm-1 resolution at a scanning interval of 1.929 cm-1. Sixty-four scans were collected per reading and averaged into one spectrum. After the first spectrum was collected for a sample, the powder was mixed, another spectrum was collected, and then this process was repeated for a total of three spectra per sample, which were then further averaged into one spectrum. Various pre-processing methods were applied to the spectral data before chemometric modeling.
|Wheat Flour Adulteration||RMSEC= 1.781%|
The results of this study were excellent, especially when considering that the samples used were from different markets. The independent predictions proved the validity of the model and showed that wheat flour adulteration in oat flour can be accurately predicted from NIR spectra and a calibration model to an accuracy of around 2%. NIR spectroscopy offers a fast, non-invasive, and cost-effective method for determining adulteration in oat flour that can be implemented for large-scale testing. Quantitative Analysis of Adulterations in Oat Flour by FT-NIR Spectroscopy, Incomplete Unbalanced Randomized Block Design, and Partial Least Squares (hindawi.com)
Discrimination of Cassava, Taro, and Wheat Flour Using Near-Infrared Spectroscopy and Chemometrics
Different species of flour can differ in many ways, such as nutritional value and market price. However, they often do not differ in appearance, making them subject to adulteration. One example of this is taro flour, which is obtained from taro tubers and has a high starch, protein, and fiber content compared to some other types of flour. In Indonesia, taro flour has a higher market price than some common species of flour, such as wheat flour and cassava flour. NIR spectroscopy was examined as a method for discriminating between taro, wheat, and cassava flours. Taro flour samples were made by obtained taro tubers which were washed, drained, sliced, dried in an oven, and then grounded and sieved to make flour. Cassava flour was prepared in a similar fashion except the tubers were not soaked in water. Wheat flour samples were purchased from a local market. Samples were scanned from 1000 nm to 2500 nm using an NIR spectrometer. Resolution was 4 cm-1. Each sample was scanned three times and the three spectra were averaged into one spectrum per sample. Various pre-treatment methods were applied to the spectral data before chemometric analysis. Principle Component Analysis (PCA) was first performed to establish a grouping pattern and examine variance between the three groups of samples. Discriminant Analysis (DA) was used to create a model to determine if the groups can be classified based on NIR spectra.
|Taro||Samples – 16||Correct Predictions – 16|
|Cassava||Samples – 13||Correct Predictions – 13|
|Wheat||Samples – 9||Correct Predictions – 9|
The results of this study were excellent and proved that NIR Spectroscopy and a classification model can be used to determine if a flour sample is taro, cassava, or wheat. The independent predictions were correct at a perfect 100% rate. NIR spectroscopy offers a fast, non-invasive, non-destructive, and accurate method for discriminating between these three types of flour. Discrimination of cassava, taro, and wheat flour using near-infrared spectroscopy and chemometrics | Rafi | Jurnal Kimia Sains dan Aplikasi (undip.ac.id)
Application of Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopy Coupled with Wavelength Selection for Fast Discrimination of Similar Color of Tuber Flours
Tubers are a major food in Indonesia and are usually consumed as an alternative food to rice. They are rich in carbohydrates and a source of a number of valuable nutritional components. They have a high metabolic activity after harvesting and are more perishable than grains. Because of this, they are usually processed into flours to prolong shelf-life. In flour form, they are used for standard foods like noodles, biscuits, snacks, and bread. Three prominent species of tubers are very similar in appearance but vary in market and nutritional value: Canna edulis, modified cassava, and white sweet potato. Visual differentiation is nearly impossible and traditional methods using chemical analysis to determine the type of tuber flour are expensive and impractical for implementing for large-scale testing. Both NIR and IR spectroscopy were examined as a method for differentiating between these three species of flour tubers. Samples of all three types of tubers were procured from ten different sellers for the study. In total, three samples were taken from each purchased flour, making for thirty samples for each type of tuber and a total of ninety samples. All samples were scanned using an FT-NIR spectrometer from 10000 cm-1 to 4000 cm-1 at 4 cm-1 resolution. Thirty-two scans were collected per reading and averaged into one spectrum. The wavenumber range for the IR spectrometer was from 4000 cm-1 to 650 cm-1. Various pre-processing methods were applied to both sets of spectral data and a PLS-DA (Partial Least Squares-Discriminant Analysis) model was created for both the NIR and IR spectral data sets.
|NIR||R2= 0.999||SEP = 0.03|
|IR||R2= 0.999||SEP = 0.08|
A PLS-DA model uses arbitrary numbers for classification analysis and as shown, the results were excellent for both the NIR and IR models. Cross-validation was used to prove the validity of the models and all predictions chose the correct type of flour at 100% accuracy. NIR and IR spectroscopy can used to classify different types of flour tubers and offer a fast and non-invasive method for properly classifying flours that cannot be easily discriminated without using expensive and time-consuming chemical tests. Application of Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopy Coupled with Wavelength Selection for Fast Discrimination of Similar Color of Tuber Flours | Masithoh | Indonesian Journal of Chemistry (ugm.ac.id)