Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis

Wu, Qianyi; Oliveira, Marciano M.; Achata, Eva M.; Kamruzzaman, Mohammed

doi:10.1016/j.jfca.2023.105324

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…The near infrared hyperspectral imaging is applicable to solid samples as well as liquid samples, thus the variety of its applications in food technologies to a large range of food products. Several studies using the near infrared hyperspectral technology have contributed to various areas of food technologies including food quality control and authentication (18,19,20,21), food safety assessment (22,23,24,25), food process monitoring and optimization (26,27,28), food shelf-life prediction (29,30), food ingredients analysis (31,32), food allergens detection (33,34), and food sorting and grading (35). These research works showed that NIR-HSI covers notable areas of food technologies.…”

Section: Near Infrared Hyperspectral Imaging In Food Technologiesmentioning

confidence: 99%

Non-destructive Near-infrared Hyperspectral Imaging in Food Technologies with a Focus on Monitoring Seed Viability

N’dri-Aya,

Vroh-Bi

2024

ijcsrr

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Seed qualities including viability and germination significantly influence the quantity and the quality of harvest. Technological means to assess seed qualities and attributes of seed-derived food products are varied. This paper highlights the use of infrared hyperspectral imaging (NIR- HSI) in food quality control, authentication, safety, process monitoring, shelf-life prediction, ingredients analysis, allergens detection and food sorting and grading. It also shows a particular application of NIR-HSI for the monitoring of nutrient content of sprouts and germinated seeds for industrial processing of foods with high nutritional values. The paper further reviews the applications of NIR-HSI to predict seed viability and germination. The non-destructive, rapid, and high-throughput capability of NIR-HSI were demonstrated through research works combining the NIR-HSI technology with chemometrics tools to reach more than 90% prediction rate. These relatively high rates may depend on the storage conditions or the stringency of the artificial aging conditions applied to parts of the seeds. However, the NIR-HSI has also proven efficient using naturally aged seeds with the prediction rates up to 90% correct classification, demonstrating the high capability of the technology. In combination with advanced chemometrics tools, some components of emerging technologies such as traditional machine learning and deep learning models have been added to increase the efficiency of NIR_HSI. Overall, the research works reviewed in this paper and which cover several food crops and food products showed that NIR-HSI is set to reach new heights in monitoring seed viability for improved seed stock management, crop production and innovation in the food industry.

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