近红外光谱(NIRS)是一种分析技术,由于其低运营成本,在食品工业中与实验室分析相比具有真正的优势。快速分析,和无损采样技术。大量研究表明,近红外光谱分析与正确校准评估某些食品特性的相关性。这使得它在质量控制和食品加工的连续监测中很有用。然而,NIR校准过程是困难和耗时的。分析方法和技术根据NIR仪器的配置而有所不同,要分析的样本和要预测的属性。这使得校准成为许多制造商的挑战。本文旨在提供一种数据驱动的方法,用于开发基于NIRS波长智能选择的决策支持工具,以评估各种食品特性。基于该方法的决策支持工具已经对可可豆样品进行了评估,小麦和芒果的谷物。对于可可豆的水分和脂肪含量,每个选定的模型都获得了有希望的结果(R2cv:0.90,R2test:0.93,RMSEP:0.354%;R2cv:0.73,R2test:0.79,RMSEP:0.913%),芒果的酸度和维生素C含量(R2cv:0.93,R2test:0.97,RMSEP:17.40%;R2cv:0.66,R2test:0.46,RMSEP:0.848%),小麦DS2的蛋白质含量(R2cv:0.90,R2test:0.92,RMSEP:0.49%)。此外,所提出的方法可以获得比小麦DS1的水分和蛋白质含量基准更好的结果(R2cv:0.90,R2test:94,RMSEP:0.337%;R2cv:0.99,R2test:0.99,RMSEP:0.177%),分别。实际应用:这项研究介绍了一种实用工具,旨在通过识别特定的光波长来确定食物的质量。然而,重要的是要承认潜在的挑战,比如过度拟合。在实施之前,对于进一步研究以解决和减轻问题以确保解决方案的可靠性和准确性至关重要。如果成功应用,该工具可以显着提高近红外光谱评估食品质量属性的准确性。这一进步将为食品生产行业的决策提供宝贵的支持,最终为消费者带来更好的整体产品质量。
Near infrared spectroscopy (NIRS) is an analytical technique that offers a real advantage over laboratory analysis in the food industry due to its low operating costs, rapid analysis, and non-destructive sampling technique. Numerous studies have shown the relevance of NIR spectra analysis for assessing certain food properties with the right calibration. This makes it useful in quality control and in the continuous monitoring of food processing. However, the NIR calibration process is difficult and time-consuming. Analysis methods and techniques vary according to the configuration of the NIR instrument, the sample to be analyzed and the attribute that is to be predicted. This makes calibration a challenge for many manufacturers. This paper aims to provide a data-driven methodology for developing a decision support tool based on the smart selection of NIRS wavelength to assess various food properties. The decision support tool based on the methodology has been evaluated on samples of cocoa beans, grains of wheat and mangoes. Promising results were obtained for each of the selected models for the moisture and fat content of cocoa beans (R2cv: 0.90, R2test: 0.93, RMSEP: 0.354%; R2cv: 0.73, R2test: 0.79, RMSEP: 0.913%), acidity and vitamin C content of mangoes (R2cv: 0.93, R2test: 0.97, RMSEP: 17.40%; R2cv: 0.66, R2test: 0.46, RMSEP: 0.848%), and protein content of wheat-DS2 (R2cv: 0.90, R2test:0.92, RMSEP: 0.490%) respectively. Moreover, the proposed approach allows results to be obtained that are better than benchmarks for the moisture and protein content of wheat-DS1 (R2cv: 0.90, R2test: 94, RMSEP: 0.337%; R2cv: 0.99, R2test: 0.99, RMSEP: 0.177%), respectively. PRACTICAL APPLICATION: This research introduces a practical tool aimed at determining the quality of food by identifying specific light wavelengths. However, it is important to acknowledge potential challenges, such as overfitting. Before implementation, it is crucial for further research to address and mitigate the issues to ensure the reliability and accuracy of the solution. If successfully applied, this tool could significantly enhance the accuracy of near-infrared spectroscopy in assessing food quality attributes. This advancement would provide invaluable support for decision-making in industries involved in food production, ultimately leading to better overall product quality for consumers.