Novel Application of Near-infrared Spectroscopy and Chemometrics Approach for Detection of Lime Juice Adulteration

• Published in: Iranian journal of pharmaceutical research : IJPR Vol. 19; no. 2; pp. 34 - 44
• Main Authors: Jahani, Reza, Yazdanpanah, Hassan, van Ruth, Saskia M, Kobarfard, Farzad, Alewijn, Martin, Mahboubi, Arash, Faizi, Mehrdad, Shojaee AliAbadi, Mohammad Hossein, Salamzadeh, Jamshid
• Format: Journal Article
• Language: English
• Published: Netherlands Shaheed Beheshti University of Medical Sciences 2020
• Subjects: Original; Portable NIR; Lime juice; Food fraud; PLS-DA; k-NN; Chemometrics
• ISSN: 1735-0328; 1726-6890
• DOI: 10.22037/ijpr.2019.112328.13686

The aim of this study is to investigate the novel application of a ‎handheld near infra-red spectrophotometer coupled with classification methodologies as a screening approach in detection of adulterated lime juices. For this purpose, a miniaturized near infra-red spectrophotometer (Tellspec ) in the spectral range of 900-1700 nm was used. Three diffuse reflectance spectra of 31 pure lime juices were collected from Jahrom, Iran and 25 adulterated juices were acquired. Principal component analysis was almost able to generate two clusters. Partial least square discriminant analysis and -nearest neighbors algorithms with different spectral preprocessing techniques were applied as predictive models. In the partial least squares discriminant analysis, the most accurate prediction was obtained with SNV transforming. The generated model was able to classify juices with an accuracy of 88% and the Matthew's correlation ‎coefficient ‎value of 0.75 in the external validation set. In the -NN model, the highest accuracy and Matthew's correlation ‎coefficient in the test set (88% and 0.76, respectively) was obtained with multiplicative signal correction followed by 2 -order derivative and 5 nearest neighbor. The results of this preliminary study provided promising evidence of the potential of the handheld near infra-red spectrometer and machine learning methods for rapid detection of lime juice adulteration. Since a limited number of the samples were used in the current study, more lime juice samples from a wider range of variability need to be analyzed in order to increase the robustness of the generated models and to confirm the promising results achieved in this study.