A non-destructive determination of protein content in potato flour noodles using near-infrared hyperspectral imaging technology

Zhang, Jing; Guo, Zhen; Ren, Zhishang; Wang, Sihua; Yin, Xiang; Zhang, Dongliang; Wang, Chenjie; Zheng, Hui; Du, Juan

doi:10.1016/j.infrared.2023.104595

Cited by 4 publications

(1 citation statement)

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“…In another study, the NIRS was used in the 900-2250 nm wavelength range to assess the protein content in potato flour noodles in a non-destructive manner. The performance of the model was evaluated through partial least squares regression (PLSR), and the results indicated a high degree of accuracy with an R 2 value of 0.8925 and RMSE value of 0.1385% for the prediction set [23]. In another investigation, the NIRS technology was used to predict the purity of the flour PLSR using six samples of authentic almond flour.…”

Section: Introductionmentioning

confidence: 99%

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

et al. 2023

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Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that approximately 1% of the population may have gluten-related disorders during their lifetime, thus, the scientific community has tried to study different methods to detect this protein. There are multiple commercial quantitative methods for gluten detection, such as enzyme-linked immunosorbent assays (ELISAs), polymerase chain reactions, and advanced proteomic methods. ELISA-based methods are the most widely used; but despite being reliable, they also have certain constraints, such as the long periods they take to detect the protein. This study focuses on developing a novel, rapid, and budget-friendly IoT system using Near-infrared spectroscopy technology, Deep and Machine Learning algorithms to predict the presence or absence of gluten in flour samples. 12,053 samples were collected from 3 different types of flour (rye, corn, and oats) using an IoT prototype portable solution composed of a Raspberry Pi 4 and the DLPNIRNANOEVM infrared sensor. The proposed solution can collect, store, and predict new samples and is connected by using a real-time serverless architecture designed in the Amazon Web services. The results showed that the XGBoost classifier reached an Accuracy of 94.52% and an F2-score of 92.87%, whereas the Deep Neural network had an Accuracy of 91.77% and an F2-score of 96.06%. The findings also showed that it is possible to achieve high-performance results by only using the 1452–1583 nm wavelength range. The IoT prototype portable solution presented in this study not only provides a valuable contribution to the state of the art in the use of the NIRS + Artificial Intelligence in the food industry, but it also represents a first step towards the development of technologies that can improve the quality of life of people with food intolerances.

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Section: Introductionmentioning

confidence: 99%