Chemometric aided NIR portable instrument for rapid assessment of medicine quality

Zontov, Y.V.; Balyklova, K.S.; Титова, А. В.; Rodionova, Oxana Ye.; Pomerantsev, Alexey L.

doi:10.1016/j.jpba.2016.08.008

Cited by 47 publications

(18 citation statements)

References 18 publications

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“…Finally, off-line analysis is performed in a central laboratory by qualified operators, but results are not available within a short time. At-line and off-line methods could have some limitations due to the need to apply changes to the production process or to have a laboratory in which to prepare the samples to be analyzed using a desktop instrument [ 17 , 18 ]. On the other hand, miniaturized NIRS is very promising for the pet food industry aiming at the control of their production processes in-line in a cost-effective manner, allowing the determination of quality control parameters rapidly and without the need for the modification of the manufacturing plant.…”

Section: Introductionmentioning

confidence: 99%

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Goi

Simoni

Righi

et al. 2020

Animals

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The aim of the present study was to investigate the ability of a handheld near-infrared spectrometer to predict total and gelatinized starch, insoluble fibrous fractions, and mineral content in extruded dry dog food. Intact and ground samples were compared to determine if the homogenization could improve the prediction performance of the instrument. Reference analyses were performed on 81 samples for starch and 99 for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and minerals, and reflectance infrared spectra (740 to 1070 nm) were recorded with a SCiO™ near-infrared (NIR) spectrometer. Prediction models were developed using modified partial least squares regression and both internal (leave-one-out cross-validation) and external validation. The best prediction models in cross-validation using ground samples were obtained for gelatinized starch (residual predictive deviation, RPD = 2.54) and total starch (RPD = 2.33), and S (RPD = 1.92), while the best using intact samples were obtained for gelatinized starch (RPD = 2.45), total starch (RPD = 2.08), and K (RPD = 1.98). Through external validation, the best statistics were obtained for gelatinized starch, with an RPD of 2.55 and 2.03 in ground and intact samples, respectively. Overall, there was no difference in prediction models accuracy using ground or intact samples. In conclusion, the miniaturized NIR instrument offers the potential for screening purposes only for total and gelatinized starch, S, and K, whereas the results do not support its applicability for the other traits.

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Section: Introductionmentioning

confidence: 99%

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Goi

Simoni

Righi

et al. 2020

Animals

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“…The main advantages of NIRS are its comparatively low cost, fast measurements and easy handling [25,26]. Furthermore, it is possible to miniaturize NIR technology and thus reduce costs and weight, as well as improve consumer-friendliness [27,28]. With the miniaturization process beginning already a decade ago, its main challenge was to preserve spectral performance in terms of wavenumber range and resolution [28].…”

Section: Introductionmentioning

confidence: 99%

Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis

et al. 2019

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The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R2) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.

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“…Food adulteration analysis is not the only application that employs class modeling for analyzing the obtained data using a variety of approaches and obtaining reliable results. These techniques also yield remarkable results in quality analysis of pharmaceuticals-there have been studies on the determination of the active pharmaceutical ingredient content [14] and the active pharmaceutical ingredient content in the intact uncoated tablets of loratadine [15].…”

Section: Introductionmentioning

confidence: 99%

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Faqeerzada

Lohumi

Joshi

et al. 2020

Foods

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Methods that combine targeted techniques and chemometrics for analyzing food authenticity can only facilitate the detection of predefined or known adulterants, while unknown adulterants cannot be detected using such methods. Therefore, the non-targeted detection of adulterants in food products is currently in great demand. In this study, FT-IR and FT-NIR spectroscopic techniques were used in combination with non-targeted chemometric approaches, such as one-class partial least squares (OCPLS) and data-driven soft independent modeling of class analogy (DD-SIMCA), to detect adulterants in almond powder adulterated with apricot and peanut powders. The reflectance spectra of 100 pure almond powder samples from two different varieties (50 each) were collected to develop a calibration model based on each spectroscopic technique; each model was then evaluated for four independent sets of two varieties of almond powder samples adulterated with different concentrations of apricot and peanut powders. Classification using both techniques was highly sensitive, the OCPLS approach yielded 90–100% accuracy in different varieties of samples with both spectroscopic techniques, and the DD-SIMCA approach achieved the highest accuracy of 100% when used in combination with FT-IR in all validation sets. Moreover, DD-SIMCA, combined with FT-NIR, achieved a detection accuracy between 91% and 100% for the different validation sets and the misclassified samples belong to the 5% and 7% adulteration sets. These results suggest that spectroscopic techniques, combined with one-class classifiers, can be used effectively in the high-throughput screening of potential adulterants in almond powder.

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Chemometric aided NIR portable instrument for rapid assessment of medicine quality

Cited by 47 publications

References 18 publications

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

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