Terahertz spectroscopy combined with data dimensionality reduction algorithms for quantitative analysis of protein content in soybeans

High-precise determination of the drought and cold resistance of forage seeds using terahertz time-domain spectroscopy

Owing to the complicated geographical locations and climates, cultivation and selection of forage seeds are challenging. For the first time, we qualitatively distinguished the drought and cold resistance of forage seeds with the time domain and refractive index spectra using terahertz (THz) time-domain spectroscopy. A multilayer structure propagation (MSP) model was developed based on the effective medium and light transport theory to reveal the underlying biological mechanisms of drought and cold resistance of forage seeds. The proposed MSP model accurately explained the behavior of the THz waves transmitted through the forage seeds, with a high accuracy rate of 94.433%. The impact of THz wave transmission was influenced by the presence of various biological components in the alfalfa seeds, particularly protein and carbohydrate. More interestingly, the cold and drought resistance of forage seeds can be effectively differentiated with the ratio of the thickness-dependent argument parameter (Ψ) of protein and carbohydrate components. The obtained results offered important insights into the interaction mechanism between THz wave and forage seeds, and proposed a promising MSP model in the screening process for selecting high-quality forage seeds based on their stress resistance characteristics.

Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology

As a crucial stage in the processing of black tea, rolling plays a significant role in both the color transformation and the quality development of the tea. In this process, the production of theaflavins, thearubigins, and theabrownins is a primary factor contributing to the alteration in color of rolled leaves. Herein, tea pigments are selected as the key quality indicators during rolling of black tea, aiming to establish rapid detection methods for them. A machine vision system is employed to extract nine color feature variables from the images of samples subjected to varying rolling times. Then, the tea pigment content in the corresponding samples is determined using a UV-visible spectrophotometer. In the meantime, the correlation between color variables and tea pigments is discussed. Additionally, Z-score and PCA are used to eliminate the magnitude difference and redundant information in original data. Finally, the quantitative prediction models of tea pigments based on the images' color features are established by using PLSR, SVR, and ELM. The data show that the Z-score-PCA-ELM model has the best prediction effect for tea pigments. The Rp values for the model prediction sets are all over 0.96, and the RPD values are all greater than 3.50. In this study, rapid determination methods for tea pigments during rolling of black tea are established. These methods offer significant technical support for the digital production of black tea.

Recent advance in nondestructive imaging technology for detecting quality of fruits and vegetables: a review

As an integral part of daily dietary intake, the market demand for fruits and vegetables is continuously growing. However, traditional methods for assessing the quality of fruits and vegetables are prone to subjective influences, destructive to samples, and fail to comprehensively reflect internal quality, thereby resulting in various shortcomings in ensuring food safety and quality control. Over the past few decades, imaging technologies have rapidly evolved and been widely employed in nondestructive detection of fruit and vegetable quality. This paper offers a thorough overview of recent advancements in nondestructive imaging technologies for assessing the quality of fruits and vegetables, including hyperspectral imaging (HSI), fluorescence imaging (FI), magnetic resonance imaging (MRI), thermal imaging (TI), terahertz imaging, X-ray imaging (XRI), ultrasonic imaging, and microwave imaging (MWI). The principles and applications of these imaging techniques in nondestructive testing are summarized. The challenges and future trends of these technologies are discussed.

Study on qualitative identification of aflatoxin solution based on terahertz metamaterial enhancement

Aflatoxin is the main carcinogen that contaminates agricultural products and foods such as peanuts and corn. There are many kinds of aflatoxins, mainly including aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1) and aflatoxin G2 (AFG2). Different types of aflatoxins have different toxicity and different levels of contamination to agricultural products as well as food. Therefore, the rapid, non-destructive and highly sensitive qualitative identification of aflatoxin species is of great significance to maintain people's life and health. The conventional terahertz detection method can only qualitatively identify the samples at the milligram level, but it is not suitable for the qualitative analysis of trace samples. In this paper, a terahertz metamaterial sensor with "X" composite double-peak structure was designed based on electromagnetic theory to investigate the feasibility of THz-TDS technology based on a metamaterial sensor for the qualitative identification of trace aflatoxin B2, G1 and G2 solutions. Firstly, the terahertz transmission spectra of eight different concentrations of aflatoxin B2, G1 and G2 were collected respectively, and then the differences of terahertz transmission spectra of different aflatoxin species were investigated. Finally, the terahertz transmission spectra of aflatoxin B2, G1 and G2 solutions were modeled and analyzed using chemometric methods. It was found that there were significant differences in the transmission peak curves of different kinds of aflatoxin. Through the comparative analysis of different models, it was concluded that the prediction accuracy of the CARS-RBF-SVM model was the highest, and the accuracy of the calibration set reached 100%. 119 out of 120 predicted samples were correctly predicted, and the prediction accuracy was 99.17%. This study verified the feasibility of qualitative identification of trace aflatoxin B2, G1 and G2 solutions by a metamaterial sensor based on the "X" composite double-peak structure combined with THz-TDS technology, and provided a theoretical basis and a new detection method for the qualitative identification of trace aflatoxins. This will facilitate the rapid, non-destructive and highly sensitive qualitative detection of different kinds of aflatoxins in food and agricultural products. At the same time, this study has important implications for promoting the qualitative detection of other trace substances.

Quantitative analysis method of Panax notoginseng based on thermal perturbation terahertz two-dimensional correlation spectroscopy

This paper proposes a Panax notoginseng (P. notoginseng) quantitative analysis based on terahertz time-domain spectroscopy and two-dimensional correlation spectroscopy (2DCOS). By imposing temperature perturbation combined with 2DCOS, the one-dimensional absorbance spectra were transformed into 2DCOS synchronous spectra, which reflected the differences in characteristic information between different P. notoginseng contents more clearly. Then, the feature information of P. notoginseng contents was extracted from the 2DCOS synchronous spectra by a competitive adaptive reweighted sampling (CARS) method and was used to build a quantitative model combined with a support vector regression machine (SVR), called 2DCOS-CARS-SVR. We obtained a more accurate analysis result than the commonly used principal component analysis (PCA)-partial least squares regression (PLSR) and PCA-SVR. The prediction set correlation coefficient and root mean square error reached 0.9915% and 0.8160%, respectively.

Research on highly sensitive quantitative detection of aflatoxin B2 solution based on THz metamaterial enhancement

Food such as cereal crops, oil crops and dairy products are very easy to produce highly toxic and carcinogenic aflatoxins during inappropriate storage. Therefore, it is of great significance to achieve rapid, non-destructive and highly sensitive detection of aflatoxin. A terahertz metamaterial sensor with "X" compound double-peak structure is designed based on electromagnetic theory to realize highly sensitive detection of aflatoxin B2 solution. It is found that the amplitude of the transmission peak of the terahertz transmission spectrum of aflatoxin B2 (AFB2) solution around 1.2 THz and 2.0 THz gradually decreased with the increase of the concentration of aflatoxin B2 solution, and the frequency of the transmission peak gradually shifted to high frequency with the increase of the concentration of aflatoxin B2 solution, hence a full concentration model was established. And a strategy of first classifying concentration intervals and then building a grouped quantitative model was proposed. The Limit of Detection (LOD) of the interval sub-model of low and medium concentration of aflatoxin B2 solution has been greatly improved with the LOD of the optimal grouping model was 7.28 × 10^-11 mg/ml, 4.19 × 10^-9 mg/ml and 1.22 × 10^-7 mg/ml, respectively. This research verifies the feasibility of terahertz metamaterial sensor based on "X" composite double-peak structure combined with THz-TDS technology for highly sensitive detection of aflatoxin B2 solution. And it provides a new rapid, non-destructive and highly sensitive detection of aflatoxin in food.

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

Applications of THz Spectral Imaging in the Detection of Agricultural Products

Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.