Study on feasibility of determination of glucosamine content of fermentation process using a micro NIR spectrometer

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

In situ rapid evaluation method of quality of peach kernels based on near infrared spectroscopy

This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.

Process understanding and monitoring: A glimpse into data strategies for miniaturized NIR spectrometers

BACKGROUND

The implementation of process analytical technologies (PAT) has gained attention since 2004 when its formal introduction through the U.S. Food and Drug Administration was introduced. Manufacturers that need to evaluate the employment of new monitoring systems could face different challenges: identification of suitable sensors, verification of data meaning, evaluation of several statistical strategies to obtain insights about data and achieve process understanding and finally, the actual possibilities for monitoring. Kefir fermentations were chosen as an example because of the chemical and physical transformations that occurred during the process, which could be common to several other fermentation processes. In order to pave the way for monitoring establish the information contained in the data and find the right tools for extracting them is of extreme importance. Strategies to identify different experimental conditions in the spectra acquired with a miniaturized NIR (1350-2550 nm) during process occurrence were addressed.

RESULTS

The study aims to offer insights into good practices and steps to pave the way for process monitoring with handheld NIR data. The main aspects of interest for batch processes in preliminary evaluations were investigated and discussed. On the one hand, process understanding and, on the other, the possibilities for process monitoring and endpoint determination were examined. The combination of different statistical tools allowed the extraction of information from the data and the identification of the link between them and the chemical and physical changes during the process. In addition, insights into the spectra characteristics in the studied spectroscopic range for kefir fermentation were reported.

SIGNIFICANCE

The capabilities for miniaturized NIR spectra to represent and statistical strategies to characterize different experimental conditions in a real case fermentation occurrence were proved. The strengths and limitations of some of the common approaches to catch changes in fermentation condition were highlighted. For the various statistical approaches, the chances offered in the research and development stages and to set the scene for monitoring and end-point detection were explored.

Rapid Quality Control of Woodchip Parameters Using a Hand-Held Near Infrared Spectrophotometer

Near infrared spectroscopy is a non-invasive and rapid technique to support the analysis of solid biofuels such as woodchip, which is considered as a suitable alternative for energy production, according to European goals for fossil fuel reduction. Chemical and physical properties of the woodchip influence combustion performance, so the most discriminant parameters such as moisture and ash content and gross calorific value were constantly monitored. The aim of this study was the development of prediction models for these three parameters with the use of a hand-held NIR spectrometer. Laboratory analyses were carried out to evaluate the quality of several Italian samples from a power plant, and PLS regression models were developed to test prediction accuracy. Moreover, the most relevant wavelengths were investigated to discriminate chemical compounds influence. Prediction models demonstrated the capacity of handheld MicroNIR instrument to be considered a practical tool for solid biofuel quality assessment. As a consequence, NIR spectroscopy improved real-time analysis and made it suitable for practical and industrial applications, as supported by the recent Italian standard UNI/TS 11765.

Rapid quality evaluation of Shenzhiling oral liquid by attenuated total reflectance-infrared spectroscopy with chemometrics

In this study, a useful method of Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with chemometrics was proposed for rapid quantification of two-indicator components as well as discrimination of Shenzhiling oral liquid in shelf life and over shelf life. Fifteen batches of samples were employed to construct quantitative and discriminant models. Two ingredients (paeoniflorin and cinnamic acid) for quality control were modeled by partial least square regression (PLSR). The discrimination of samples between in shelf life and over shelf life was carried out by using discriminant analysis (DA). The samples were divided into calibration set and validation set according to batches. Different data pre-processing algorithms such as standard-normal-variate (SNV), multiplicative scatter correction (MSC), Savitzkye-Golay (SG) smoothing with derivative methods were applied to reduce the influence of systematic disturbances. Variable selection methods including correlation coefficient (CC), competitive adaptive reweighted sampling (CARS) and interval partial least squares regression (iPLS) were all performed for optimizing the PLSR models and DA model. The results demonstrated that ATR-FTIR combined with chemometrics could be a rapid, convenient and nondestructive approach to evaluate the quality of Shenziling oral liquid.

Data mean and ratio of absorbance to concentration methods: A novel optimization strategy for near infrared spectroscopy modeling

For the modeling of near infrared spectroscopy (NIRS), the accuracy of the basic data, the stability of the spectra and the optimality of variables selection method were the important factors. In this paper, a novel optimization strategy for NIRS modeling was proposed, which was formed by data mean and ratio of absorbance to concentration (RATC) methods. The data mean method was aim to obtain accurate basic data and stable spectra, the RATC method was aim to select the optimal variables and compared with other variables selection methods (FiPLS, BiPLS, CC, UVE). The experimental subject was raw human plasma, with this novel optimization strategy, the predictive capability of NIRS model of its total protein (TP) content had been improved. At the same time, the public NIRS testing data (water, protein, oil, starch of corn and octane of gasoline) were used to verify the proposed variables selection method, and the predictive capability of these models of different parameters were also improved. To some extent, the optimization strategy of NIRS modeling provided theoretical supports for the development of protein content analyzer of NIRS and the quick determination of parameters of biologics and other materials.