Detection of fraud in lime juice using pattern recognition techniques and FT‐IR spectroscopy

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.

Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach

“…In our previous study, we revealed the capability of another portable NIRS (Tellspec ® , 900-1700 nm) and chemometrics approach for the discrimination of genuine and citric-adulterated lime juices with the accuracy of 88% for each PLS-DA and k-NN models (29). In addition, the feasibility of FT-IR spectroscopy and chemometrics approach in the detection of adulterated lime juice (prepared by lime juice concentrates) was revealed in a study conducted by Mohammadian et al The lime juice samples were correctly designated to their original groups using PLS-DA and counter propagation artificial neural networks (CPANN) maps with an overall accuracy of 87% in the validation procedure (12).…”

“…For instance: The Chinese milk scandal where the milk products and infant formula were adulterated with melamine (4,5), the contamination of chili powder with dye (6), several cases of the adulteration of spices with ground materials (7), the Irish pork crisis (8), the horse-meat scandal (9), adulteration of olive oil with hazelnut oil (10) and honey made from an artificial sweetener (11) are just some examples. Besides, several cases of adulteration in fruit juices have been detected in recent years (12,13).…”

Machine Learning Enabled Nanosensor Array for Monitoring Citrus Juice Adulteration

“…For example, high-performance liquid chromatography (HPLC) , and Fourier transform infrared spectroscopy (FTIR) , have been tested for citrus juice authenticity when coupled with machine learning. Specifically, FTIR has been evaluated against a variety of pure or adulterated fruit juice samples. − However, a common limitation to untargeted instrumental approaches is the requirement of expensive instrumentation to have variable turnover rates, which were generally demonstrated using limited sample sizes. ,,,,, Thus, it is necessary to devise novel and alternative approaches with low-cost, simplistic, and high-throughput capabilities for rapid screening. Recently, the combination of nanotechnology with nucleic acids has made tremendous ground for medicinal and biosensing applications. − An advantage of nanoparticle-based biosensor formulations is the bulk scale production using low-cost natural precursors, and inexpensive simple benchtop spectrometers for data collection, which can expedite untargeted detection efforts.…”

A Novel Strategy for Improving the Counter Propagation Artificial Neural Networks in Classification Tasks

“…The purpose of choosing this model is its capability to combine both unsupervised and supervised learning. In Addition, the CP-ANNs are a popular network [16] and are extensively used in numerous areas [17][18][19] through their abilities to solve the issues of classification, clustering, and recognition tasks. Moreover, pattern recognition comprises two learning modes (Unsupervised and supervised) [20].…”