Search Results (4)

Protein content is a key quality indicator in nuts, influencing their color, taste, storage, and processing properties. Traditional methods for protein quantification, such as the Kjeldahl nitrogen method, are time-consuming and destructive, highlighting the need for rapid, convenient alternatives. This study explores the feasibility of using portable near-infrared spectroscopy (NIRS) for the quantitative prediction of protein content in Torreya grandis (T. grandis) kernels by comparing different sample states (with shell, without shell, and granules). Spectral data were acquired using a portable NIR spectrometer, and the protein content was determined via the Kjeldahl nitrogen method as a reference. Outlier detection was performed using principal component analysis combined with Mahalanobis distance (PCA-MD) and concentration residual analysis. Various spectral preprocessing techniques and partial least squares regression (PLSR) were applied to develop protein prediction models. The results demonstrated that portable NIRS could effectively predict protein content in T. grandis kernels, with the best performance being achieved using granulated samples. The optimized model (1Der-SNV-PLSR-G) significantly outperformed models based on whole kernels (with or without shell), with determination coefficients for the calibration set ($R_c^2$) and prediction set ($R_p^2$) of 0.92 and 0.86, respectively, indicating that the sample state critically influenced prediction accuracy. This study confirmed the potential of portable NIRS as a rapid and convenient tool for protein quantification in nuts, offering a practical alternative to conventional methods. The findings also suggested its broader applicability for quality assessment in other nuts and food products, contributing to advancements in food science and agricultural technology. Full article

Torreya grandis kernels, with their long cultivation history and significant economic value, have gained attention for their characteristic chemical components. This review systematically evaluates recent research on the chemical constituents and biological activities of T. grandis kernels. The key highlights include the following. (1) Chemical composition: This review details their unique fatty acid profile, particularly the high content of unsaturated fatty acids and rare polymethylene-interrupted polyunsaturated fatty acids such as sciadonic acid. It also examines polyphenolic compounds (flavonoids, phenolic acids, and biflavonoids like kayaflavone) and volatile components dominated by D-limonene. Other constituents, such as proteins, amino acids, vitamins, and minerals, are covered. Advanced analytical techniques (Gas Chromatography–Mass Spectrometry, GC-MS; Liquid Chromatography–Tandem Mass Spectrometry, LC-MS/MS) for component identification are discussed. (2) Biological activities: This review summarizes the major biological activities of T. grandis kernel extracts and key components. These include antioxidant effects (via the polyphenol-mediated NF-E2-related factor 2 (Nrf2) pathway), anti-inflammatory properties (via polymethylene-interrupted polyunsaturated fatty acids, PMI-PUFAs, inhibition of 5-LOX, and polyphenol regulation of NF-κB), and cardiovascular protection (potentially involving the AMPKα/SREBP-1c pathway). Research on gut microbiota regulation and enzyme inhibition is also outlined. (3) Research gaps and prospects: This review critically analyzes the limitations in the current research, including mechanism elucidation, component interactions, bioavailability, and safety assessment (especially the lack of human studies). Future research directions should focus on multiomics integration, structure–activity relationship analysis, standardization, and rigorous clinical evaluation. This review provides a theoretical reference for understanding the scientific value of T. grandis kernels and promoting their sustainable development. Full article

Water content is an important parameter of Torreya grandis (T. grandis) kernels that affects their quality, processing and storage. The traditional drying method for water content determination is time-consuming and laborious. Water content detection based on modern analytical techniques such as spectroscopy is accomplished in a fast, accurate, nondestructive, and sustainable way. The aim of this study was to realize the rapid detection of the water content in T. grandis kernels using near-infrared spectroscopy. The water content of T. grandis kernels was measured by the traditional drying method. Meanwhile, the corresponding near-infrared spectra of these samples were collected. A quantitative water content model of T. grandis kernels was established using the full spectrum after 10 outlier samples were removed by the Mahalanobis distance method and concentration residual analysis. The results showed that the prediction model developed from the partial least squares regression (PLS) method after the spectra were pretreated by the standard normal variate transform (SNV) achieved optimal performance. The correlation coefficient of the calibration set (R²c) and the cross-validation set (R²cv) were 0.9879 and 0.9782, respectively, and the root mean square error of the calibration set (RMSEC) and the root mean square error of the cross-validation set (RMSECV) were 0.0029 and 0.0039, respectively. Thus, near-infrared spectroscopy is feasible for the rapid nondestructive detection of the water content in T. grandis seeds. Detecting the water content of agricultural and forestry products in such an environmentally friendly manner is conducive to the sustainable development of agriculture. Full article

To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times. Full article