Handheld and benchtop vis/NIR spectrometer combined with PLS regression for fast prediction of cocoa shell in cocoa powder

The fermented and dried cocoa beans are peeled, either before or after the roasting process, as peeled nibs are used for chocolate production, and shell content in cocoa powders may result from economically motivated adulteration (EMA), cross-contamination or misfits in equipment in the peeling process. The performance of this process is carefully evaluated, as values above 5% (w/w) of cocoa shell can directly affect the sensory quality of cocoa products. In this study chemometric methods were applied to near-infrared (NIR) spectra from a handheld (900-1700 nm) and a benchtop (400-1700 nm) spectrometers to predict cocoa shell content in cocoa powders. A total of 132 binary mixtures of cocoa powders with cocoa shell were prepared at several proportions (0 to 10% w/w). Partial least squares regression (PLSR) was used to develop the calibration models and different spectral preprocessing were investigated to improve the predictive performance of the models. The ensemble Monte Carlo variable selection (EMCVS) method was used to select the most informative spectral variables. Based on the results obtained with both benchtop (R²_P = 0.939, RMSE_P = 0.687% and RPD_P = 4.14) and handheld (R²_P = 0.876, RMSE_P = 1.04% and RPD_P = 2.82) spectrometers, NIR spectroscopy combined with the EMCVS method proved to be a highly accurate and reliable tool for predicting cocoa shell in cocoa powder. Even with a lower predictive performance than the benchtop spectrometer, the handheld spectrometer has potential to specify whether the amount of cocoa shell present in cocoa powders is in accordance with the Codex Alimentarius specifications.

Overfitting One-Dimensional convolutional neural networks for Raman spectra identification

Dedicated handheld spectrometers have been adopted by first responders and law enforcement agencies for in situ identification of unknown substances. Real-time spectral matching process is a pixel-by-pixel comparing of the unknown spectra with reference data. In fact, the success rate of this process using a miniaturized portable Raman spectrometer relies mainly on the variety of reference data carried on the memory. This is a hurdle in miniaturizing and affordability of the current handheld spectrometers due to limited memory and computational power. In this study, we aim to mitigate this issue by utilizing the power of one-dimensional Convolutional Neural Networks (1DCNN) trained on millions of Raman spectra augmented from standard available reference databases. Specifically, an intentionally overfitted 1DCNN model can be substituted with the reference database of handheld spectrometers to alleviate the memory size and increase the identification process speed and accuracy. Our experimental results revealed that 1DCNN could identify one pure unknown Raman instance from thousands of classes with a high accuracy.

Overfitting One-Dimensional convolutional neural networks for Raman spectra identification

Dedicated handheld spectrometers have been adopted by first responders and law enforcement agencies for in situ identification of unknown substances. Real-time spectral matching process is a pixel-by-pixel comparing of the unknown spectra with reference data. In fact, the success rate of this process using a miniaturized portable Raman spectrometer relies mainly on the variety of reference data carried on the memory. This is a hurdle in miniaturizing and affordability of the current handheld spectrometers due to limited memory and computational power. In this study, we aim to mitigate this issue by utilizing the power of one-dimensional Convolutional Neural Networks (1DCNN) trained on millions of Raman spectra augmented from standard available reference databases. Specifically, an intentionally overfitted 1DCNN model can be substituted with the reference database of handheld spectrometers to alleviate the memory size and increase the identification process speed and accuracy. Our experimental results revealed that 1DCNN could identify one pure unknown Raman instance from thousands of classes with a high accuracy.

Hierarchical classification models and Handheld NIR spectrometer to human blood stains identification on different floor tiles

One of the most important types of evidence in certain criminal investigations is traces of human blood. For a detailed investigation, blood samples must be identified and collected at the crime scene. The present study aimed to evaluate the potential of the identification of human blood in stains deposited on different types of floor tiles (five types of ceramics and four types of porcelain tiles) using a portable NIR instrument. Hierarchical models were developed by combining multivariate analysis techniques capable of identifying traces of human blood (HB), animal blood (AB) and common false positives (CFP). The spectra of the dried stains were obtained using a portable MicroNIR spectrometer (Viavi). The hierarchical models used two decision rules, the first to separate CFP and the second to discriminate HB from AB. The first decision rule, used to separate the CFP, was based on the Q-Residual criterion considering a PCA model. For the second rule, used to discriminate HB and AB, the Q-Residual criterion were tested as obtained from a PCA model, a One-Class SIMCA model, and a PLS-DA model. The best results of sensitivity and specificity, both equal to 100%, were obtained when a PLS-DA model was employed as the second decision rule. The hierarchical classification models built for these same training sets using a PCA or SIMCA model also obtained excellent sensitivity results for HB classification, with values above 94% and 78% of specificity. No CFP samples were misclassified. Hierarchical models represent a significant advance as a methodology for the identification of human blood stains at crime scenes.