Article
Spectroscopy
Huan Zhang, Xiaoyun Hu, Limei Liu, Junfu Wei, Xihui Bian
Summary: This study successfully utilized near infrared spectroscopy combined with chemometrics to quantitatively analyze the corn oil content in binary to hexanary edible blend oils. By employing spectral preprocessing and variable selection techniques, accurate prediction models were developed to determine the pure oil content in the blend oils.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Spectroscopy
Gerard Dumancas, Indra Adrianto
Summary: This study developed a stacked regression ensemble approach using near infrared spectroscopic method for accurate determination of biomass compositional analyses. The performance of various machine learning techniques was compared, and the stacked regression outperformed other methods, providing a more accurate prediction of biomass compositions.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Review
Chemistry, Analytical
Puneet Mishra, Dario Passos, Federico Marini, Junli Xu, Jose M. Amigo, Aoife A. Gowen, Jeroen J. Jansen, Alessandra Biancolillo, Jean Michel Roger, Douglas N. Rutledge, Alison Nordon
Summary: This paper provides a critical and comprehensive review of the major benefits and potential pitfalls of current deep learning techniques used for spectral data modeling. Although it focuses on near-infrared (NIR) spectral data in chemometric tasks, many of the findings can be applied to other spectral techniques. Finally, empirical guidelines on the best practices for using deep learning for the modeling of spectral data are provided.
TRAC-TRENDS IN ANALYTICAL CHEMISTRY
(2022)
Article
Chemistry, Analytical
Puneet Mishra, Junli Xu, Kristian Hovde Liland, Thanh Tran
Summary: This paper introduces a novel approach called META-PLS for automatic NIR spectral modeling. The approach utilizes the stepwise nature of PLS algorithm and a weighted randomization test to avoid exhaustive search for preprocessing selection and the optimal number of model components.
ANALYTICA CHIMICA ACTA
(2022)
Article
Chemistry, Analytical
Pan-pan Yang, Zhong-da Zeng, Ying Hou, Ai-ming Chen, Juan Xu, Long-qing Zhao, Xiang-yi Liu
Summary: The rapid and accurate authentication of variants of plants with high chemical complexity is of great academic and practical significance. The combination of near infrared spectroscopy (NIR) and chemometrics, specifically partial least squares discrimination analysis (PLS-DA), proved to be an effective method for classification and prediction. Various preprocessing techniques and feature selection methods were compared to optimize the PLS-DA models, achieving high prediction accuracy.
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
(2023)
Article
Environmental Sciences
Yizhi Shi, Liang Yi, Guorong Du, Xi Hu, Yue Huang
Summary: Microplastics pose potential hazards to human health and have gained increasing attention. While most studies focus on the characterization of microplastics in the environment, there is limited research on microplastics in solid foods. This study used three molecular spectral imaging approaches combined with chemometrics to characterize the presence of microplastics in corn flour. The results showed that the combination of Raman imaging and independent component analysis provided a better depiction of microplastics. Overall, this study demonstrates the potential of molecular spectral imaging for visual detection of microplastics in powdered food and provides a reference for the detection of microplastics in other foods.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Food Science & Technology
Sonia Nieto-Ortega, Silvia Mas Garcia, Angela Melado-Herreros, Giuseppe Foti, Idoia Olabarrieta, Jean-Michel Roger
Summary: This study investigates the kinetics and mechanisms involved in the cooking process of b & eacute;chamel sauces using a hand-held near infrared spectroscopy (NIRS) sensor and multivariate curve resolution-alternating least-squares (MCR-ALS) approach. It was found that the kinetic constants of the sauces varied depending on the initial temperature of the cooking process. The results provide a new strategy for studying the manufacturing process of b & eacute;chamel sauces in a nondestructive way.
FOOD AND BIOPROCESS TECHNOLOGY
(2023)
Article
Green & Sustainable Science & Technology
So-Yeon Jeong, Eun-Ju Lee, Se-Eun Ban, Jae-Won Lee
Summary: Near infrared (NIR) spectroscopy is a rapid, accurate, and non-destructive method for analyzing biomass composition, but prediction accuracy varies for different biomass compositions. Different biomass particle sizes have statistically significant differences in NIR spectra based on root mean square values, and preprocessing methods do not significantly improve prediction accuracy.
Article
Agronomy
Puneet Mishra, Maxence Paillart, Lydia Meesters, Ernst Woltering, Aneesh Chauhan
Summary: This study confirms that skin dehydration negatively affects the performance of Vis-NIR calibrations for avocado fruit analysis. The differences in measurement modes had varying impacts on dehydrated and non-dehydrated fruit, with the interaction mode performing better for non-dehydrated fruit. The limitations of Vis-NIR spectroscopy for analyzing dehydrated avocado fruit were evident in the study.
POSTHARVEST BIOLOGY AND TECHNOLOGY
(2022)
Article
Agriculture, Multidisciplinary
Leandro Levate Macedo, Cintia da Silva Araujo, Wallaf Costa Vimercati, Paulo Ricardo Gherardi Hein, Carlos Jose Pimenta, Sergio Henriques Saraiva
Summary: This study used NIR technology combined with PLS regression to quickly estimate the chemical properties of green coffee, showing different predictive capabilities. The best model was obtained for predicting grain moisture, while the worst performance was observed for the soluble solids model.
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
(2021)
Article
Food Science & Technology
Denis Yegon, Nelson K. Ojijo, Thorsten Tybussek, Willis Owino
Summary: In this study, a portable NIR spectroscopy combined with chemometrics was used for the detection of BFPP adulteration. The results showed that two-class models had high reliability, with sensitivity and specificity above 0.98 and error below 0.01. Four-class models achieved sensitivity and specificity above 0.68 and error below 0.276. The PLSR models had correlation coefficients above 0.88 and root mean square error below 6.20%, with LODs below 13.79%. Therefore, NIR spectroscopy has promising potential for rapid screening of BFPP adulterations.
INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY
(2023)
Article
Food Science & Technology
Jamille Carvalho Souza, Celio Pasquini, Maria C. Hespanhol
Summary: The study evaluated two low-cost, miniaturized near-infrared (NIR) spectrophotometers in assessing several traits of commercial ground roasted coffee. The results show that these instruments have the potential to characterize coffee qualitatively and quantitatively, with a certain ability for discrimination and prediction.
Article
Chemistry, Analytical
Claire McVey, Terry F. McGrath, Simon A. Haughey, Christopher T. Elliott
Summary: This study evaluated the use of a handheld near infrared spectroscopy device for determining oregano authenticity, achieving high prediction accuracy for both authentic oregano and adulterants. The research showed that the device could potentially be a cost-effective and reliable tool in detecting food fraud and monitoring food authenticity throughout the supply chain.
Article
Food Science & Technology
Huseyin Ayvaz, Fatma Korkmaz, Havva Polat, Zayde Ayvaz, Necati Baris Tuncel
Summary: Einkorn wheat, an ancient grain with limited production, is often adulterated due to its high price and weaker gluten structure. Both Computer-Based Image (CBI) Analysis and Near-Infrared Spectroscopy (NIRS) methods were found effective in screening wheat flour adulteration in einkorn bread and flour mixtures, with NIRS being more recommended for flour mixtures.
Review
Food Science & Technology
Huiwen Yu, Lili Guo, Mourad Kharbach, Wenjie Han
Summary: NIRS is a fast and powerful analytical tool in the food industry, with multi-way analysis showing potential for solving complex data problems. The combination of multi-way analysis with NIRS in the food industry is expected to turn data into operational knowledge and improve the reliability of food analyses.