1. Detection of fraud in lime juice using pattern recognition techniques and FT‐IR spectroscopy
- Author
-
Mohsen Barzegar, Ahmad Mani-Varnosfaderani, and Amirhossein Mohammadian
- Subjects
Citrus aurantifolia ,FT‐IR spectroscopy ,01 natural sciences ,0404 agricultural biotechnology ,TX341-641 ,Cluster analysis ,Original Research ,Mathematics ,Lime Juice ,Nutrition. Foods and food supply ,business.industry ,010401 analytical chemistry ,Counter propagation ,modeling ,Pattern recognition ,04 agricultural and veterinary sciences ,Citrus limetta ,040401 food science ,0104 chemical sciences ,classification ,Principal component analysis ,Pattern recognition (psychology) ,Ft ir spectroscopy ,Artificial intelligence ,lime juice ,business ,artificial neural networks ,Food Science - Abstract
The lime juice is one of the products that has always fallen victim to fraud by manufacturers for reducing the cost of products. The aim of this research was to determine fraud in distributed lime juice products from different factories in Iran. In this study, 101 samples were collected from markets and also prepared manually and finally derived into 5 classes as follows: two natural classes (Citrus limetta, Citrus aurantifolia), including 17 samples, and three reconstructed classes, including 84 samples (made from Spanish concentrate, Chinese concentrate, and concentrate containing adulteration compounds). The lime juice samples were freeze‐dried and analyzed using FT‐IR spectroscopy. At first, principal component analysis (PCA) was applied for clustering, but the samples were not thoroughly clustered with respect to their original groups in score plots. To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square‐discriminant analysis (PLS‐DA), and counter propagation artificial neural networks (CPANN) were used. The best discriminatory wavenumbers related to each class were selected using the VIP‐PLS‐DA algorithm. Then, the CPANN algorithm was used as a nonlinear mapping tool for classification of the samples based on their original groups. The lime juice samples were correctly designated to their original groups in CPANN maps and the overall accuracy of the model reached up to 0.96 and 0.87 for the training and validation procedures. This level of accuracy indicated the FT‐IR spectroscopy coupled with VIP‐PLS‐DA and CPANN methods can be used successfully for detection of authenticity of lime juice samples., In this work, the authenticity of commercial lime juice was detected and quantified using FT‐IR spectroscopy coupled with the VIP variable selection and CPANN models. The main advantage of the present contribution is the diversity of the calibrating samples which include broad ranges of natural, synthetic, and adulterated lime juice samples. Therefore, applicability domain of the developed discriminative model in this work would be broad and wide which is a needed property in fraud detection in lime juice industry.
- Published
- 2021