Reza Jahani, Saskia van Ruth, Yannick Weesepoel, Martin Alewijn, Farzad Kobarfard, Mehrdad Faizi, Mohammad Hossain Shojaee AliAbadi, Arash Mahboubi, Azadeh Nasiri, Hassan Yazdanpanah
{"title":"便携式和台式近红外光谱仪检测柠檬酸掺假酸橙汁的比较:化学计量学方法。","authors":"Reza Jahani, Saskia van Ruth, Yannick Weesepoel, Martin Alewijn, Farzad Kobarfard, Mehrdad Faizi, Mohammad Hossain Shojaee AliAbadi, Arash Mahboubi, Azadeh Nasiri, Hassan Yazdanpanah","doi":"10.5812/ijpr-128372","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Since the incidence of food adulteration is rising, finding a rapid, accurate, precise, low-cost, user-friendly, high-throughput, ruggedized, and ideally portable method is valuable to combat food fraud. Near-infrared spectroscopy (NIRS), in combination with a chemometrics-based approach, allows potentially rapid, frequent, and in situ measurements in supply chains.</p><p><strong>Methods: </strong>This study focused on the feasibility of a benchtop Fourier-transformation-NIRS apparatus (FT-NIRS, 1000 - 2500 nm) and a portable short wave NIRS device (SW-NIRS, 740 - 1070 nm) for the discrimination of genuine and citric acid-adulterated lime juice samples in a cost-effective manner following chemometrics study.</p><p><strong>Results: </strong>Principal component analysis (PCA) of the spectral data resulted in a noticeable distinction between genuine and adulterated samples. Wavelengths between 1100 - 1400 nm and 1550 - 1900 nm were found to be more important for the discrimination of samples for the benchtop FT-NIRS data, while variables between 950 - 1050 nm contributed significantly to the discrimination of samples based on the portable SW-NIRS data. Following partial least squares discriminant analysis (PLS-DA) as a discriminant model, standard normal variate (SNV) or multiplicative scatter correction (MSC) transformation of benchtop FT-NIRS data and SNV in combination with the second derivative transformation of portable SW-NIRS data on the training set delivered equal accuracy (94%) in the prediction of the test set. In the soft independent modeling of class analogy (SIMCA) as a class-modeling approach, the overall performances of generated models on the auto-scaled data were 98% and 94.5% for benchtop FT-NIRS and portable SW-NIRS, respectively.</p><p><strong>Conclusions: </strong>As a proof of concept, NIRS technology coupled with appropriate multivariate classification models enables fast detection of citric acid-adulterated lime juices. In addition, the promising results of portable SW-NIRS combined with SIMCA indicated its use as a screening tool for on-site analysis of lime juices at various stages of the food supply chain.</p>","PeriodicalId":14595,"journal":{"name":"Iranian Journal of Pharmaceutical Research","volume":"21 1","pages":"e128372"},"PeriodicalIF":1.8000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/be/82/ijpr-21-1-128372.PMC10024328.pdf","citationCount":"0","resultStr":"{\"title\":\"Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach.\",\"authors\":\"Reza Jahani, Saskia van Ruth, Yannick Weesepoel, Martin Alewijn, Farzad Kobarfard, Mehrdad Faizi, Mohammad Hossain Shojaee AliAbadi, Arash Mahboubi, Azadeh Nasiri, Hassan Yazdanpanah\",\"doi\":\"10.5812/ijpr-128372\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Since the incidence of food adulteration is rising, finding a rapid, accurate, precise, low-cost, user-friendly, high-throughput, ruggedized, and ideally portable method is valuable to combat food fraud. Near-infrared spectroscopy (NIRS), in combination with a chemometrics-based approach, allows potentially rapid, frequent, and in situ measurements in supply chains.</p><p><strong>Methods: </strong>This study focused on the feasibility of a benchtop Fourier-transformation-NIRS apparatus (FT-NIRS, 1000 - 2500 nm) and a portable short wave NIRS device (SW-NIRS, 740 - 1070 nm) for the discrimination of genuine and citric acid-adulterated lime juice samples in a cost-effective manner following chemometrics study.</p><p><strong>Results: </strong>Principal component analysis (PCA) of the spectral data resulted in a noticeable distinction between genuine and adulterated samples. Wavelengths between 1100 - 1400 nm and 1550 - 1900 nm were found to be more important for the discrimination of samples for the benchtop FT-NIRS data, while variables between 950 - 1050 nm contributed significantly to the discrimination of samples based on the portable SW-NIRS data. Following partial least squares discriminant analysis (PLS-DA) as a discriminant model, standard normal variate (SNV) or multiplicative scatter correction (MSC) transformation of benchtop FT-NIRS data and SNV in combination with the second derivative transformation of portable SW-NIRS data on the training set delivered equal accuracy (94%) in the prediction of the test set. In the soft independent modeling of class analogy (SIMCA) as a class-modeling approach, the overall performances of generated models on the auto-scaled data were 98% and 94.5% for benchtop FT-NIRS and portable SW-NIRS, respectively.</p><p><strong>Conclusions: </strong>As a proof of concept, NIRS technology coupled with appropriate multivariate classification models enables fast detection of citric acid-adulterated lime juices. 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Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach.
Background: Since the incidence of food adulteration is rising, finding a rapid, accurate, precise, low-cost, user-friendly, high-throughput, ruggedized, and ideally portable method is valuable to combat food fraud. Near-infrared spectroscopy (NIRS), in combination with a chemometrics-based approach, allows potentially rapid, frequent, and in situ measurements in supply chains.
Methods: This study focused on the feasibility of a benchtop Fourier-transformation-NIRS apparatus (FT-NIRS, 1000 - 2500 nm) and a portable short wave NIRS device (SW-NIRS, 740 - 1070 nm) for the discrimination of genuine and citric acid-adulterated lime juice samples in a cost-effective manner following chemometrics study.
Results: Principal component analysis (PCA) of the spectral data resulted in a noticeable distinction between genuine and adulterated samples. Wavelengths between 1100 - 1400 nm and 1550 - 1900 nm were found to be more important for the discrimination of samples for the benchtop FT-NIRS data, while variables between 950 - 1050 nm contributed significantly to the discrimination of samples based on the portable SW-NIRS data. Following partial least squares discriminant analysis (PLS-DA) as a discriminant model, standard normal variate (SNV) or multiplicative scatter correction (MSC) transformation of benchtop FT-NIRS data and SNV in combination with the second derivative transformation of portable SW-NIRS data on the training set delivered equal accuracy (94%) in the prediction of the test set. In the soft independent modeling of class analogy (SIMCA) as a class-modeling approach, the overall performances of generated models on the auto-scaled data were 98% and 94.5% for benchtop FT-NIRS and portable SW-NIRS, respectively.
Conclusions: As a proof of concept, NIRS technology coupled with appropriate multivariate classification models enables fast detection of citric acid-adulterated lime juices. In addition, the promising results of portable SW-NIRS combined with SIMCA indicated its use as a screening tool for on-site analysis of lime juices at various stages of the food supply chain.
期刊介绍:
The Iranian Journal of Pharmaceutical Research (IJPR) is a peer-reviewed multi-disciplinary pharmaceutical publication, scheduled to appear quarterly and serve as a means for scientific information exchange in the international pharmaceutical forum. Specific scientific topics of interest to the journal include, but are not limited to: pharmaceutics, industrial pharmacy, pharmacognosy, toxicology, medicinal chemistry, novel analytical methods for drug characterization, computational and modeling approaches to drug design, bio-medical experience, clinical investigation, rational drug prescribing, pharmacoeconomics, biotechnology, nanotechnology, biopharmaceutics and physical pharmacy.