The paper aims to propose “Portable NIRS Technology for Thai Food Industry” with applications using non-destructive based technology called Near-Infrared Spectroscopy (NIRS) for determining whether the sampled food is expired or not. This proposed NIRS technology will be applicable especially for the agricultural and food industries. The basic process of the proposed system can be divided into the following two parts, i.e., 1) a sensor device that detects samples and 2) a mobile application developed for connecting to the NIRS device. The binary k-nearest neighbors algorithm (k-NN) is proposed to classify the five common Thai local samples, i.e., soy milk (SM), fresh milk (FM), salad dressing (SD), corn milk (CM), and banana in coconut milk (BM). The output results are classified into two classes, i.e., Unexpired (Un) and expired (Ex), based on the wavelength measuring from the proposed NIRS system.

Development of Mobile Application Monitoring for Thai Food and Beverage Based on Portable Near-Infrared Spectroscopy Technology

The main objective of this study is to estimate the potential of near infrared (NIR) spectroscopy as a rapid and non-destructive tool for the detection as well as quantification of sudan dye I adulterated turmeric powder samples. Pure turmeric powder was prepared at the laboratory and spiked with Sudan dye I and investigated accordingly. The concentrations of the adulterants were 0.05%, 0.1%, 0.2%, 0.5%, 1%, 1.5%, 2%, 5%, 10%, 15%, 20%, 25% and 30% (w/w), respectively. Exploratory data analysis was done for the visualization of the adulterant classes by principal component analysis (PCA). In the classification approach, principal components (PCs) extracted by PCA were fed as the inputs of the support vector machine (SVM) classifier. The average accuracy of the adulterants classes noted was greater than 90%.

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Papers

Identification and Classification of Sudan Dye I Adulterants in Turmeric Powder by Nir Spectroscopy and Support Vector Machine