A novel method for ash analysis in wheat milling fractions by using laser-induced breakdown spectroscopy

A Rapid Detection of Whole Wheat Gluten Quality by a Novel Chemometric Technique-GlutoPeak

The study aims to accurately detect gluten quality of whole wheat flour without a refining process by measuring gluten aggregation properties with a novel and non-destructive chemometric technique called GlutoPeak, coupled with principal component analyses (PCA) and hierarchical cluster analyses (HCA). For this purpose, whole wheat flour samples from 125 common bread wheat cultivars were analyzed for protein content (PC), wet gluten content (WGC), and Zeleny sedimentation value (SV). The correlations of GlutoPeak indices (peak maximum time, PMT; maximum torque, MT; torque 15 s before MT, AM; torque 15 s after MT) with other conventional wheat quality parameters were evaluated. Results indicated that MT had high correlations with WGC (r = 0.627, p < 0.05) and PC (r = 0.589, p < 0.05) while PC (r = 0.511, p < 0.05) and WGC (r = 0.566, p < 0.05) values had moderate correlations with the GlutoPeak PM index. Considering the effect of regions, the MT and PM GlutoPeak indices are powerful parameters to discriminate whole wheat flour samples by their gluten strengths. In conclusion, the GlutoPeak test can be a powerful and reliable tool for prediction of refined and unrefined wheat quality without being time-consuming.

Analysis of corn and sorghum flour mixtures using laser-induced breakdown spectroscopy

BACKGROUND

In a world constantly challenged by climate change, corn and sorghum are two important grains because of their high productivity and adaptability, and their multifunctional use for different purposes such as human food, animal feed, and feedstock for many industrial products and biofuels. Corn and sorghum can be utilized interchangeably in certain applications; one grain may be preferred over the other for several reasons. The determination of the composition corn and sorghum flour mixtures may be necessary for economic, regulatory, environmental, functional, or nutritional reasons.

RESULTS

Laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics, was used for the classification of flour samples based on the LIBS spectra of flour types and mixtures using partial least squares discriminant analysis (PLS-DA) and the determination of the sorghum ratio in sorghum / corn flour mixture based on their elemental composition using partial least squares (PLS) regression. Laser-induced breakdown spectroscopy with PLS-DA successfully identified the samples as either pure corn, pure sorghum, or corn-sorghum mixtures. Moreover, the addition of various levels of sorghum flour to mixtures of corn-sorghum flour were used for PLS analysis. The coefficient of determination values of calibration and validation PLS models are 0.979 and 0.965, respectively. The limit of detection of the PLS models is 4.36%.

CONCLUSION

This study offers a rapid method for the determination of the sorghum level in corn-sorghum flour mixtures and the classification of flour samples with high accuracy, a short analysis time, and no requirement for time-consuming sample preparation procedures. © 2020 Society of Chemical Industry.

Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination

A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92-100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.

Quantification of Ash and Moisture in Wheat Flour by Raman Spectroscopy

Wheat flour is widely used on an industrial scale in baked goods, pasta, food concentrates, and confectionaries. Ash content and moisture can serve as important indicators of the wheat flour’s quality and use, but the routinely applied assessment methods are laborious. Partial least squares regression models, obtained using Raman spectra of flour samples and the results of reference gravimetric analysis, allow for fast and reliable determination of ash and moisture in wheat flour, with relative standard errors of prediction of the order of 2%. Analogous calibration models that enable quantification of carbon, oxygen, sulfur, and nitrogen, and hence protein, in the analyzed flours, with relative standard errors of prediction equal to 0.1, 0.3, 3.3, and 1.4%, respectively, were built combining the results of elemental analysis and Raman spectra.

High-sensitivity determination of cadmium and lead in rice using laser-induced breakdown spectroscopy

Stability and sensitivity of toxic elements determination is still unsatisfactory in agricultural product using laser-induced breakdown spectroscopy (LIBS). A simple and low cost sample pretreatment method named solid-liquid-solid transformation method was proposed in this work. The target analytes of cadmium (Cd) and lead (Pb) from rice samples were prepared through ultrasound assisted extraction in hydrochloric acid solution. The solution was dropped on the glass slide after centrifuging process and was further dried on a heater. Finally, the glass slide contained the analytes was carried out for LIBS determination. Compare with conventional pellet method, the spectral intensity of Cd and Pb element were enhanced significantly using LIBS. The limits of detection were 2.8 and 43.7 μg/kg, respectively. The limits of quantification were 9.3 and 145.7 μg/kg, respectively. The results demonstrated that LIBS coupled with ultrasound assisted extraction should be a promising tool to detect toxic elements in rice.

Analytical-performance improvement of laser-induced breakdown spectroscopy for the processing degree of wheat flour using a continuous wavelet transform

Quality and safety of food are two of the most important matters in our lives. Wheat is one of the most important products in the modern agricultural processing industry. Issues of mislabeling and adulteration are of increasingly serious concern in the grain market. They threaten the credibility of producers and traders and the rights of the consumers. Therefore, it is very significant to guarantee the processing degree of wheat flour. In this work, two different spectral peak recognition methods, i.e., artificial spectral peak recognition and automatic spectral peak recognition, are carried out to study the adulteration problem in the food industry. Three grades of the processing degree of wheat flour from northern China are classified by laser-induced breakdown spectroscopy (LIBS). To search for an automatic classification model, continuous wavelet transform is used for the automatic recognition of the LIBS spectrum peak. Principal component analysis is used to reduce the collinearity of LIBS spectra data. First, 20 principal components were selected to represent the spectral data for the following discrimination analysis by a support vector machine. The results showed that the classification accuracies of automatic spectral peak recognition are better than those of artificial spectral peak recognition. The classification accuracies of artificial spectral peak recognition and automatic spectral peak recognition are 95.33% and 98.67%; the fivefold cross-validation classification accuracies are 94.67% and 96.67%; and the operation times were 240 min and 2 min, respectively. It can be concluded that LIBS can provide simpler and faster classification without the use of any chemical reagent, which represents a decisive advantage for applications dedicated to rapidly detecting the processing degree of wheat flour and other cereals.