Analysis and detection using novel terahertz spectroscopy technique in dietary carbohydrate-related research: Principles and application advances

The influence of processing technologies on the biological activity of carbohydrates in food

Food processing transforms raw materials into different food forms using physical or chemical techniques. Recently, carbohydrates have gained attention for their diverse biological activities like antioxidant, anticancer, and antimutagenic effects. Selecting suitable processing methods is crucial to preserve the beneficial properties of carbohydrates. This review discusses the impact of non-thermal and thermal processing on the physicochemical and biological traits of carbohydrates, highlighting the need for understanding the mechanisms underlying these changes. Future research will focus on enhancing and safeguarding the biological and functional aspects of carbohydrates through improved processing techniques. The goal is to optimize methods that maintain the beneficial properties of carbohydrates, maximizing their health benefits for consumers.

Quantification of resistant starch content in rice after hydrothermal treatments using terahertz spectroscopy

Since hydrothermal treatments can enhance resistant starch (RS) content in rice and provide health benefits when consumed, a less laborious and non-destructive method to determine RS content is needed. Terahertz (THz) spectroscopy is hypothesized as a suitable method to quantify RS content in rice after hydrothermal treatment with its sensitivity for the intermolecular forces increase in the formation of RS. In this study, we first used the traditional in vitro hydrolysis method to determine the content of RS in rice. Then, the potential of starch absorbance peaks to quantify RS content after three commonly used hydrothermal methods, soaking, mild heat-moisture treatment, and parboiling, was investigated. The second derivative intensities of the peak at 9.0, 10.5, 12.1, and 13.1 THz were confirmed as being correlated with RS content and showed the high accuracy to predict RS content in samples (R2 > 0.96). Our results indicate the RS content of hydrothermally treated rice can be accurately quantified using these peaks.

Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages

Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.

Terahertz Spectroscopic Identification of Roast Degree and Variety of Coffee Beans

In this study, terahertz time-domain spectroscopy (THz-TDS) was proposed to identify coffee of three different varieties and three different roasting degrees of one variety. Principal component analysis (PCA) was applied to extract features from frequency-domain spectral data, and the extracted features were used for classification prediction through linear discrimination (LD), support vector machine (SVM), naive Bayes (NB), and k-nearest neighbors (KNN). The classification effect and misclassification of the model were analyzed via confusion matrix. The coffee varieties, namely Catimor, Typica 1, and Typica 2, under the condition of shallow drying were used for comparative tests. The LD classification model combined with PCA had the best effect of dimension reduction classification, while the speed and accuracy reached 20 ms and 100%, respectively. The LD model was found with the highest speed (25 ms) and accuracy (100%) by comparing the classification results of Typica 1 for three different roasting degrees. The coffee bean quality detection method based on THz-TDS combined with a modeling analysis method had a higher accuracy, faster speed, and simpler operation, and it is expected to become an effective detection method in coffee identification.