Application of mid-infrared spectroscopy and PLS-Kernel calibration for quick detection of pork in higher value meat mixes

Optimizing the quantitative analysis of solid biomass fuel properties using laser induced breakdown spectroscopy (LIBS) coupled with a kernel partial least squares (KPLS) model

The rapid analysis of fuel properties is important for the utilization of solid biomass due to its great variation in feedstock. Laser-induced breakdown spectroscopy (LIBS) technology combined with quantitative analysis models can be used for this analysis. Most existing prediction models used in LIBS for fuel property analysis are linear methods, such as the partial least squares (PLS) model, which fail to reflect the non-linear relationships between the LIBS spectrum and the fuel property index being predicted. In the present work, LIBS data combined with a kernel partial least squares (KPLS) method are used to analyze the gross calorific value, and the volatile matter, ash and fixed carbon content of the solid biomass fuel. Quantitative analysis performance of the KPLS model was compared to that of the widely used PLS method, with the results showing some improvements. The KPLS model was further improved using three data normalization methods (i.e., C internal standardization, total intensity standardization and standard normal variate). The best quantitative analysis results of the volatile matter and ash content were obtained when the KPLS model was combined with C internal standardization, with root mean square errors of prediction (RMSEP) of 1.365% and 0.290%, and average standard deviations (ASD) of 0.277% and 0.080%, respectively. The best quantitative analysis results of the gross calorific value and fixed carbon content were obtained when using KPLS without normalization. The RMSEP and ASD of the gross calorific value and fixed carbon content were 0.198 MJ kg^-1 and 0.746%, and 0.070 MJ kg^-1 and 0.111% respectively.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Comparative review and the recent progress in detection technologies of meat product adulteration

Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.

FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of artificial sweeteners

Fourier transform infrared (FTIR) spectroscopy calibrations were developed to simultaneously determine the multianalytes of five artificial sweeteners, including sodium cyclamate, sucralose, sodium saccharin, acesulfame-K and aspartame. By combining the pretreatment of the spectrum and principal component analysis, 131 feature wavenumbers were extracted from the full spectral range for modelling to qualitative and quantitative analysis. Compared to random forest, k nearest neighbour and linear discriminant analysis, support vector machine model had better predictivity, indicating the most effective identification performance. Furthermore, multivariate calibration models based on partial least squares regression were constructed for quantifying any combinations of the five artificial sweeteners, and validated by prediction data sets. As shown by the good agreement between the proposed method and the reference HPLC for the determination of the sweeteners in beverage samples, a promising and rapid tool based on FTIR spectroscopy, coupled with chemometrics, has been performed to identify and objectively quantify artificial sweeteners.