Construction of global and robust near-infrared calibration models based on hybrid calibration sets using Partial Least Squares (PLS) regression

NIR quantitative model trans-scale calibration from small scale to pilot scale via directed DOSC-SBC algorithm

In order to solve the problem of inapplicability of NIR quantitative models due to the large difference between the modeling samples and the samples to be tested, Directed DOSC-SBC（DDOSC-SBC）algorithm is proposed in this paper based on Direct Orthogonal Signal Correction combined with Slope/Bias Correction (DOSC-SBC) algorithm. To obtain the suitable spectral matrix transfer parameters for the test set during DDOSC spectral preprocessing, several representative test samples in the test set were selected, then the spectral systematic errors between the modeling set and the test set were corrected with the SBC method in order to realize the trans-scale prediction of the NIR quantitative model. NIR data and the critical quality attributes（CQAs）were detected in the small scale and pilot scale pharmaceutical process of the fluidized bed granulation of dextrin and water extraction of honeysuckle. After the small scale model was calibrated via the directed DOSC-SBC algorithm which was guided by representative pilot scale samples, the small scale model was able to predict the pilot scale test samples more accurately. The NIR quantitative model trans-scale calibration from small scale to pilot scale was also successfully realized with a RPD value higher than 3.5 and RSEP value lower than 10%. DDOSC-SBC algorithm is a successful model trans-scale calibrated method that can be applied to NIR real-time monitoring of CQAs in the preparation process of Chinese herbal medicine.

Non-destructive prediction of the hotness of fresh pepper with a single scan using portable near infrared spectroscopy and a variable selection strategy

There has been no study on using near-infrared spectroscopy (NIRS) to predict the hotness of fresh pepper. This study is aimed at developing a non-destructive and accurate method for determining the hotness of fresh peppers using portable NIRS and the variable selection strategy. Spectra from different locations on samples were obtained non-destructively with a single scan. Quantitative models were established using partial least squares (PLS) with a variable selection method or fusion method. The results showed that near-stalk was the best spectral acquisition location for quantitative analysis. The variable selection strategy allows the selection of targeted characteristic variables and improves the results. A fusion method, namely variable adaptive boosting partial least squares (VABPLS), was selected for optimal prediction of the performance. In the optimized model, the root mean square errors of prediction for the validation set (RMSEPvs) of capsaicin, dihydrocapsaicin and pungency degree were 0.295, 0.143 and 47.770, respectively, while the root mean square errors of prediction for the prediction set (RMSEPps) collected one month later were 0.273, 0.346 and 75.524, respectively.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

The method of calibration model transfer by optimizing wavelength combinations based on consistent and stable spectral signals

Basing on the wavelengths with consistent and stable spectral signals between spectrometers, wavelength combinations were screened by different methods to obtain robust and simple near infrared spectra (NIR) calibration models that can be shared by slave spectrometers directly. Firstly, the wavelength set of Usc, at which the spectral signals between spectrometers are consistent and stable, was obtained by the method of screening the wavelengths with consistent and stable signals between spectrometers (SWCSS for short). Then, the wavelength set of Uscr whose spectral responses are correlated with dependent variables strongly was selected from Usc. Basing on Uscr, the methods of uninformative variable elimination (UVE), variable importance in projection (VIP) and selectivity ratio (SR) were applied to further screen optimal wavelength sets to obtain better NIR calibration models. These sets were recorded as Uscr^UVE, Uscr^VIP and Uscr^SR, respectively. The NIR partial least squares (PLS) models for predicting total alkaloids content of tobacco leaves were built on the three optimal wavelength sets, and named as Uscr^UVE-PLS, Uscr^VIP-PLS, Uscr^SR-PLS, respectively. Both Uscr^UVE-PLS and Uscr^VIP-PLS give satisfactory prediction errors for master and slave samples, and work better than the PLS model built on the whole wavelengths (WW-PLS) after piecewise direct standardization (PDS) calibration. The results show that further optimizing wavelength combinations based on consistent and stable spectral information cannot only simplify PLS models and improve the models' efficiency, but also ensure the models' accuracy when they are transferred to slave spectrometers. Wavelength selection based on the whole wavelengths without considering spectra consistency between spectrometers can improve the performance of the calibration models on the master spectrometer but cannot ensure the prediction accuracy of the slave samples.