Edible Gelatin Diagnosis Using Laser-Induced Breakdown Spectroscopy and Partial Least Square Assisted Support Vector Machine

Methods for detection and quantification of gelatin from different sources

Gelatin is a water-soluble protein obtained from the collagen of various animal origins (porcine, bovine, fish, donkey, horse, and deer hide) and has diverse applications in the food, pharmaceutical, and cosmetics industries. Porcine and bovine gelatins are extensively used in food and non-food products; however, their acceptance is limited due to religious prohibitions, whereas fish gelatin is accepted in all religions. In Southeast Asia, especially in China, gelatin obtained from donkey and deer skins is used in medicines. However, both sources suffer from adulteration (mixing different sources of gelatin) due to their limited availability and high cost. Unclear labeling and limited information about actual gelatin sources in gelatin-containing products cause serious concern among societies for halal and fraud authentication of gelatin sources. Therefore, authenticating gelatin sources in gelatin-based products is challenging due to close similarities between the composition differences and degradation of DNA and protein biomarkers in processed gelatin. Thus, different methods have been proposed to identify and quantify different gelatin sources in pharmaceutical and food products. To the best of our knowledge, this systematic and comprehensive review highlights different authentication techniques and their limitations in gelatin detection and quantification in various commercial products. This review also describes halal authentication and adulteration prevention strategies of various gelatin sources, mainly focussing on research gaps, challenges, and future directions in this research area.

To Estimate Performance of Artificial Neural Network Model Based on Terahertz Spectrum: Gelatin Identification as an Example

It is a necessity to determine significant food or traditional Chinese medicine (TCM) with low cost, which is more likely to achieve high accurate identification by THz-TDS. In this study, feedforward neural networks based on terahertz spectra are employed to predict the animal origin of gelatins, whose adaption to the mission is examined by parallel models built by random sample partition and initialization. It is found that the generalization performance of feedforward ANNs in original data is not satisfactory although prediction on trained samples can be accurate. A multivariate scattering correction is conducted to enhance prediction accuracy, and 20 additional models verify the effectiveness of such dispose. A special partition of total dataset is conducted based on statistics of parallel models, whose influence on ANN performance is investigated with another 20 models. The performance of the models is unsatisfactory because of notable differences in training and test sets according to principal component analysis. By comparing the distribution of the first two principal components before and after multivariate scattering correction, we found that the reciprocal of the minimum number of line segments required for error-free classification in 2-D feature space can be viewed as an index to describe linear separability of data. The rise of proposed linear separability would have a lower requirement for harsh parameter tuning of ANN models and tolerate random initialization. The difference in principal components of samples between a training set and a data set determines whether partition is acceptable or whether a model would have generality. A rapid way to estimate the performance of an ANN before sufficient tuning on a classification mission is to compare differences between groups and differences within groups. Given that a representative peak missing curve is discussed in this article, an analysis based on gelatin THz spectra may be helpful for studies on some other feature-less species.

Improving Calibration Strategy for LIBS Heavy Metals Analysis in Agriculture Applications

A new calibration procedure, known as mapping conditional-calibration laser-induced breakdown spectroscopy (LIBS), has been suggested to improve analysis results for heterogeneous samples. The procedure is based on LIBS elemental mapping, followed by signal conditioning in every sampling spot to skip signal outliers, then by finalizing the calibration curve construction. The suggested mapping conditional calibration procedure was verified for zinc analysis in soybean grist samples. The laser parameters correspond to those of the hand-held LIBS instrument in order to estimate the influence of sample surface heterogeneity under on-site analysis conditions. The laser spot (60 μm) was equal to or smaller than the typical size of grist particles (40–500 μm) but laser crater dimensions were significantly greater and varied widely (150–450 μm). The LIBS mapping of different spectral signals (atomic and ionic lines for major and minor components) was achieved. Elemental maps were normalized to achieve signal maps that were conditionally spotted to skip signal outliers. It was demonstrated that the suggested mapping conditional-calibration LIBS provided 15 ppm RMSECV for zinc determination in heterogeneous samples, which is typical for agricultural products.