Introduction
There are different potential methods for adulteration in flour and wheat products. Most wheat grown for human consumption is classified as either durum wheat or bread wheat. Durum is considered a higher quality product and a certain level of bread wheat in durum wheat constitutes adulteration. Ricin is a poison that can be fatal in small doses if consumption of milled seeds occurs, as castor bean meal (CBM) seeds that contain ricin must be milled for intestinal absorption. This creates the potential for food supply contamination with ricin in flour and flour-based products using CBM seeds. Gluten is a wide-spread food ingredient found in numerous products that has many different uses. People with celiac disease must keep gluten eliminated from their diets at all times. While standards for gluten-free food do not mean that the product is actually 100% gluten-free, it is important to determine that products marketed as such contain none or very little gluten. Current quality tests for finding adulterants in flour products are often expensive, time-consuming, and ill-suited for large scale testing. NIR spectroscopy offers an alternative to current methods and the results of some studies are documented below.
Analytes
Products:
- Durum Wheat
- Flour
- Gluten-Free Food
Adulterants:
- Bread Wheat
- Castor Bean Meal
- Caffeine
- Cane Sugar
- Corn Meal
- Gluten
Scientific References and Statistics
Durum Wheat Adulteration Detection by NIR Spectroscopy Multivariate Calibration – Cocchi, Durante, Foca – Talanta 68 (2006) 1505-1511
Wheat grown for human consumption is generally divided into two species: durum wheat and bread wheat. They are characterized by different chemical and physical properties that result in differences in quality, nutritional composition, and commercial value. In Italy, France, and Spain, law dictates that pasta can only be made from durum wheat semolina and water. In northern European countries, both durum and bread wheat can be used to make pasta. Addition of bread wheat flour to durum wheat flour as an adulterant leads to a product with a scarce resistance to cooking, resulting in lower quality. A 3% threshold of bread wheat in durum wheat is allowed to account for accidental contamination during harvesting, transport, or storage. Any higher percentage is considered unacceptable. The current official Italian method for determining bread wheat adulteration is the Resmini method, based on the electrophoretical separation of albumins. It has an uncertainty of +/- 1% in the 2% to 15% range and higher uncertainty for greater amounts of adulterant. Other tests exist but they are either expensive and time-consuming or only able to detect the presence of bread wheat adulterant without quantifying it. NIR spectroscopy was examined as a method for measuring bread wheat flour adulterant in durum wheat flour. Equal quantities of bread wheat and durum wheat were cleaned, conditioned to make the moisture consistent, and sieved to make the particles homogeneous. Twenty-nine mixtures were prepared of durum wheat flour with portions of bread wheat flour added from 0% to 7% w/w at 0.25% intervals. Samples were carefully weighed, homogenized, and prepared in duplicate before NIR spectra were collected. All samples were scanned from 400 nm to 2498 nm at 2 nm intervals. Two separate Partial Least Squares (PLS) models were created to correlate the NIR spectra to the percentage of bread wheat flour adulteration. The first used Standard Normal Variate (SNV) pre-processing. The second used the WILMA algorithm in conjunction with PLS, a signal processing method that uses the Wavelet Transform (WT) to choose selective wavelengths.
| SNV-PLS | RMSEP = 0.2904% |
| WILMA-PLS | RMSEP = 0.3276% |
Both models showed excellent results and were validated by independent predictions from samples that were pulled from the models and then predicted using the NIR spectra and the calibrations. While SNV-PLS did show a lower RMSEP and error in the independent predictions, the WILMA-PLS did not require the SNV processing and achieved comparable results using a much more limited number of wavelengths for the calibration. In either case, both models showed much lower error than the standard Resmini method used in Italy, demonstrating the potential to use NIR spectroscopy and calibration models as a real-time screening tool to detect bread wheat flour adulterant in durum wheat flour. In practice, more varieties of both types of wheat flour would need to be added to the calibration model to ensure the model is robust enough to account for differences in variety of wheat.
https://www.sciencedirect.com/science/article/pii/S0039914005004960
Use of Fourier Transform Near-Infrared Reflectance Spectroscopy for Rapid Quantification of Castor Bean Meal in a Selection of Flour-Based Products – Rodriguez-Saona, Fry, Calvey, Journal of Agriculture and Food Chemistry 2000, 48, 5169-5177
The seeds of the castor plant contain the highly potent cytotoxic protein ricin. Cases have been reported of acute intoxication in adults by consuming two to fifteen castor bean seeds and death is estimated to occur at a ratio of 1 mg/kg body weight. One to three seeds could be fatal in children. While consumption of whole castor bean meal (CBM) seeds does not lead to intoxication, consuming the milled product leads to intoxication due to the accessibility of ricin for intestinal absorption. A real threat exists for contamination of the food supply with ricin and flour would present an appealing target for the addition of CBM seeds before milling. NIR spectroscopy was examined as a method for detecting castor bean meal in flour-containing products as well as detecting the presence of other types of adulterants. Enriched bleach and wheat flour, blueberry pancake mix, granulated cane sugar, corn meal, pasteurized dried egg white, and tofu were procured from local supermarkets for the study. Bleached flour was spiked with sugar and corn meal at levels from 2% to 20% w/w and with caffeine from 0.1% to 8%. Bleached flour, wheat flour, and blueberry pancake mix were contaminated with prepared CBM from 1% to 8%. Wheat flour and blueberry pancake mix were also contaminated with other protein sources such as egg white, defatted soybean, and infant formula. NIR spectra were collected using an FT-NIR spectrometer from 10000 cm-1 to 4000 cm-1 at 4 cm-1 intervals and using 8 cm-1 resolution. Before chemometric analysis, all NIR spectra were transformed by Multiplicative Scatter Correction (MSC) to minimize scatter effects. Partial Least Squares (PLS) regression models were created using the NIR spectra and reference values of the different adulterants. The results are presented below.
Contaminated Bleach Flour (Caffeine, Cane Sugar, Corn Meal, CBM)
| R² Range = 0.89 – 0.98 | RMSEP Range = 0.36% – 2.15% |
Flour Contaminated with CBM
| Bleached Flour | R² = 0.963 | RMSEP = 0.48% |
| Wheat Flour | R² = 0.945 | RMSEP = 0.55% |
| Blueberry Pancake Mix | R² = 0.958 | RMSEP = 0.38% |
The results of all PLS models were excellent and proved the feasibility of using NIR spectroscopy for detecting the presence of CBM in flour products and if detected, determining an accurate quantification for the amount of CBM present. The models for other types of adulterants in bleach flour also showed good results. Analysis was performed using the PLS models for CBM in all three types of flour to determine if the sample was contaminated with CBM or one of the other protein-based contaminants (egg white, defatted soybean, and infant formula). The models could determine whether CBM was the adulterant present in the sample and then perform an accurate measurement on the amount. The potential exists to use NIR spectroscopy as a screening tool for determining the presence of adulterants in flour products and could replace the current expensive and time-consuming methods.
https://pubs.acs.org/doi/pdf/10.1021/jf000604m
Application of NIR Spectroscopy in Gluten Detection as a Cross-Contaminant in Food – Radman, Jurina, Benkovic, et al., Croatian Journal of Food Technology, Biotechnology and Nutrition 13 (3-4), 120-127 (2018)
Wheat and wheat products are the main source of gluten that are consumed in large quantities all around the world. Gluten is also present in barley and rye. It is widely used due to its availability, appealing taste and texture, and its ability to maintain moisture as well as enhance flavor and texture in processed foods. It can be used in liquid type foods such as ice cream, butter, and sauces as a thickening agent, emulsifier, or gelling agent. Gluten can also be separated from wheat or from wheat modifications to create vital wheat gluten or isolated wheat proteins to improve the structure of bakery products or enrichment of low-protein flour. However, it is known that gluten can lead to celiac diseases and other health problems for some people. Disorders caused by gluten intake can be divided into three types: autoimmune, allergic, and immunologically medicated. Those suffering from celiac disease must exclude all foods containing gluten from their diet for life. Because of the massive amount of foods containing gluten and the processing of it, there is a large risk of contamination of gluten-free products. Numerous tests of foods labeled as gluten-free have shown that products like oats and breakfast cereals can have a contamination rate well over 20%. “Gluten-free” is defined as containing less than 20 mg/kg of gluten and “very low gluten content” as less than 100 mg/kg. NIR spectroscopy presents a possible analytical method for large-scale and fast testing of food products for gluten contamination. The objective of this study was to determine the feasibility of using NIR spectroscopy for gluten detection using six different basic raw food materials. Two of these (wheat flour, both fine and coarse) contain gluten and were used to contaminate the other four groups. These four groups do not naturally contain gluten: rice, rice flour, corn grits, and corn flour. Of these, only rice flour is labelled as “gluten-free”. The fine and coarse wheat flour were added separately to the four groups not containing gluten in the following ratios: 5%, 10%, 15%, 20%, 25%, and 30%. NIR absorbance spectra were collected in triplicate of all samples from 904 nm to 1699 nm. The NIR spectra were used with the reference values for gluten containing contaminant to create Partial Least Squares (PLS) regression models. Shown below are the correlation coefficient (R²) and ratio of standard error of performance to standard deviation (RPD) for all eight calibration models.
PLS
| Rice | Fine Wheat Flour | R² = 0.941 | RPD= 4.117 |
| Rice | Coarse Wheat Flour | R² = 0.941 | RPD= 4.124 |
| Rice Flour | Fine Wheat Flour | R² = 0.958 | RPD= 4.880 |
| Rice Flour | Coarse Wheat Flour | R² = 0.965 | RPD= 5.345 |
| Corn Flour | Fine Wheat Flour | R² = 0.982 | RPD= 7.454 |
| Corn Flour | Coarse Wheat Flour | R² = 0.989 | RPD= 9.535 |
| Corn Grits | Fine Wheat Flour | R² = 0.950 | RPD= 4.472 |
| Corn Grits | Coarse Wheat Flour | R² = 0.982 | RPD= 7.454 |
The results of all models were excellent and proved the feasibility of determining gluten contamination in rice, rice flour, corn flour, and corn grits. For a model to have good quantitative prediction odds, the correlation coefficient should be 0.95 or higher and the RPD should be over 3, criteria that were met in all models in this study with the exception of the rice models. However, both rice models have a correlation coefficient close to the threshold at 0.941 and the RPD for both was over 4, indicating that these models should work well enough for practical use. While these results are promising, more calibration work would be necessary to apply these models in a real setting. The sensitivity threshold for both gluten-free and very low gluten content would likely not be met in a real setting as these models only contain data points at 5% intervals. Samples would need to be added at much smaller concentrations to determine if this threshold of sensitivity can be met from predictions using NIR spectroscopy. The study here provides a foundation for future research to create more sensitive and robust calibration models for determining gluten contamination in food products.