Comprehensive comparison of multiple quantitative near-infrared spectroscopy models for Aspergillus flavus contamination detection in peanut

A natural pigment-based nanosized colorimetric sensor for freshness evaluation of aquatic products

This work proposes an innovative method for monitoring the freshness of aquatic products using a nanosized colorimetric sensor (NCS). Six porous organic frameworks (POFs) were utilised to modify and sensitize four food-friendly natural pigments. Four optimal nano-pigments were selected based on their RGB-responsive interaction with NH3. Meanwhile, pigments exhibited significant changes in Vis-NIR spectra after nanosizing. Results indicated that the optimal NCS performs well in predicting aquatic products' total volatile alkaline nitrogen (TVB-N) content. While the prediction model based on image data did not benefit from nanosizing, the model constructed from spectral data demonstrated high accuracy and stability. The best PLS model achieved a prediction set correlation coefficient (Rp) of 0.9891 and a residual prediction deviation value of 6.66 by combining with variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) algorithm. Thus, the nanofabrication of POFs on pigments shows promise for developing high-precision and stable freshness prediction models.

Machine learning-based non-destructive terahertz detection of seed quality in peanut

Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively.

Prediction of moisture content for a single maize kernel based on viscoelastic properties

BACKGROUND

The rapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from the use of costly equipment, stringent environmental requirements, or limited accuracy. This study proposes a simple and effective method for detecting the moisture content of single maize kernels based on viscoelastic properties.

RESULTS

Two types of viscoelastic experiments were conducted involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency-sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force-time graphs and viscoelastic parameters were extracted based on the four-element Maxwell model. Then, viscoelastic parameters and data of force-time graphs were employed as input variables to explore the relationships with moisture content separately. The impact of different preprocessing methods and feature time variables on model accuracy was explored based on force-time graphs. The results indicate that models utilizing the force-time data were more accurate than those utilizing viscoelastic parameters. The best model was established by partial least squares regression based on S-G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set were 0.954 and 0.021, respectively. The corresponding values of the prediction set were 0.905 and 0.029, respectively.

CONCLUSIONS

This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry.

A rapid extraction process monitoring of Swertia mussotii Franch. With near infrared spectroscopy

Swertia mussotii Franch. (SMF), a traditional Tibetan medicine, which has miraculous effect on treating hepatitis diseases. However, there is no research on its entire production process, and invisible production process has seriously hindered the SMF modern development. In this study, principal component analysis (PCA), subtractive spectroscopy, and two-dimensional correlation spectroscopy (2D-COS) were used to explain changes of characteristic groups in the extraction process. Four main characteristic peaks at 1884 nm, 1944 nm, 2246 nm and 2308 nm were identified to describe the changes of molecular structure information of total active components in SMF extraction process. In addition, multi critical quality attributes (CQAs) models were established by near-infrared spectroscopy (NIRS) combined with the total quantum statistical moment (TQSM). The coefficients of determination (R²_eval and R²_ival) were both greater than 0.99. The ratios of the standard deviation of validation to the standard error of the prediction (RPD_e and RPD_i) were greater than five. The quantitative model of AUC_T could save time on primary data measurement by not requiring determination of indicator components compared with others. In conclusion, these results demonstrated that it was feasible to understand the SMF extraction process through AUC_T and characteristic groups. These could realize the visual digital characterization and quality stability of the SMF extraction process.

Accurate and non-destructive monitoring of mold contamination in foodstuffs based on whole-cell biosensor array coupling with machine-learning prediction models

Mold contamination in foodstuffs causes huge economic losses, quality deterioration and mycotoxin production. Thus, non-destructive and accurate monitoring of mold occurrence in foodstuffs is highly required. We proposed a novel whole-cell biosensor array to monitor pre-mold events in foodstuffs. Firstly, 3 volatile markers ethyl propionate, 1-methyl-1 H-pyrrole and 2,3-butanediol were identified from pre-mold peanuts using gas chromatography-mass spectrometry. Together with other 3 frequently-reported volatiles from Aspergillus flavus infection, the volatiles at subinhibitory concentrations induced significant but differential response patterns from 14 stress-responsive Escherichia coli promoters. Subsequently, a whole-cell biosensor array based on the 14 promoters was constructed after whole-cell immobilization in calcium alginate. To discriminate the response patterns of the whole-cell biosensor array to mold-contaminated foodstuffs, optimal classifiers were determined by comparing 6 machine-learning algorithms. 100 % accuracy was achieved to discriminate healthy from moldy peanuts and maize, and 95 % and 98 % accuracy in discriminating pre-mold stages for infected peanuts and maize, based on random forest classifiers. 83 % accuracy was obtained to separate moldy peanuts from moldy maize by sparse partial least square determination analysis. The results demonstrated high accuracy and practicality of our method based on a whole-cell biosensor array coupling with machine-learning classifiers for mold monitoring in foodstuffs.

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner. The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed.

Multi critical quality attributes monitoring of Chinese oral liquid extraction process with a spectral sensor fusion strategy

The traditional Chinese medicine (TCM) extraction process is a complicated dynamic system with many variables and disturbance. Therefore, multi critical quality attributes (CQAs) monitoring is of great significance to understand the whole process. Spectroscopy is a powerful process analytical tool used for process understanding. However, single senor sometimes could not provide comprehensive information. Sensor fusion is a very practical method to overcome this deficiency. In this study, the extraction process of Xiao'er Xiaoji Zhike Oral Liquid (XXZOL) was carried out in pilot scale, where near infrared (NIR) spectroscopy and mid infrared (MIR) spectroscopy were collected to determine the concentrations of seven CQAs (synephrine, arecoline, chlorogenic acid, forsythoside A, naringin, hesperidin and neohesperidin) during extraction process. Based on fused data blocks, fusion partial least squares (PLS) models were established. Two fusion data blocks are obtained from the concatenation of original spectra (low-level data fusion) and the concatenation of characteristic variables based on band selection (mid-level data fusion) respectively. The results indicated that for all seven analytes, the mid-level data fusion models were superior to the single spectral models, with the prediction performance significantly improved. Specifically, the coefficients of determination (R_p² and R_t²) of NIR, MIR and fusion quantitative models were all higher than 0.95. The relative standard errors of prediction (RSEP) values were all within 10%, except for models of neohesperidin, which were 10.76%, 12.39%, 12.05%, 10.03% for NIR, MIR, low-level and mid-level models respectively. These results demonstrate that it is feasible to monitor the extraction process of Xiao'er Xiaoji Zhike Oral Liquid more accurately and rapidly by fusing NIR and MIR spectroscopy, and the proposed approach also has vital and valuable reference value for the rapid monitoring of the mixed decoction process of other TCM.

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

Dried Hami jujube has great commercial and nutritional value. Starch-head and mildewed fruit are defective jujubes that pose a threat to consumer health. A novel method for detecting starch-head and mildewed fruit in dried Hami jujubes with visible/near-infrared spectroscopy was proposed. For this, the diffuse reflectance spectra in the range of 400–1100 nm of dried Hami jujubes were obtained. Borderline synthetic minority oversampling technology (BL-SMOTE) was applied to solve the problem of imbalanced sample distribution, and its effectiveness was demonstrated compared to other methods. Then, the feature variables selected by competitive adaptive reweighted sampling (CARS) were used as the input to establish the support vector machine (SVM) classification model. The parameters of SVM were optimized by the modified reptile search algorithm (MRSA). In MRSA, Tent chaotic mapping and the Gaussian random walk strategy were used to improve the optimization ability of the original reptile search algorithm (RSA). The final results showed that the MRSA-SVM method combined with BL-SMOTE had the best classification performance, and the detection accuracy reached 97.22%. In addition, the recall, precision, F₁ and kappa coefficient outperform other models. Furthermore, this study provided a valuable reference for the detection of defective fruit in other fruits.

Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography

Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 °C or relative humidity of approximately 90%, mold-contaminated peanuts have a considerable likelihood to be infected with Aflatoxins. Aflatoxins are known to be highly carcinogenic, posing danger to humans and livestock. In this work, we proposed a new approach for detection of mold-contaminated peanuts at an early stage. The approach employs the optical coherence tomography (OCT) imaging technique and an error-correcting output code (ECOC) based Support Vector Machine (SVM) trained on features extracted using a pre-trained Deep Convolutional Neural Network (DCNN). To this end, mold-contaminated and uncontaminated peanuts were scanned to create a data set of OCT images used for training and evaluation of the ECOC-SVM model. Results showed that the proposed approach is capable of detecting mold-contaminated peanuts with respective accuracies of approximately 85% and 96% after incubation periods of 48 and 96 h.

Curcuma longa L. Rhizome Essential Oil from Extraction to Its Agri-Food Applications. A Review

Curcuma longa L. rhizome essential oil is a valuable product in pharmaceutical industry due to its wide beneficial health effects. Novel applications in the agri-food industry where more sustainable extraction processes are required currently and safer substances are claimed for the consumer are being investigated. This review provides information regarding the conventional and recent extraction methods of C. longa rhizome oil, their characteristics and suitability to be applied at the industrial scale. In addition, variations in the chemical composition of C. longa rhizome and leaf essential oils regarding intrinsic and extrinsic factors and extraction methods are also analysed in order to select the most proper to obtain the most efficient activity. Finally, the potential applications of C. longa rhizome oil in the agri-food industry, such as antimicrobial, weedicide and a food preservative agent, are included. Regarding the data, C. longa rhizome essential oil may play a special role in the agri-food industry; however, further research to determine the application threshold so as not to damage crops or affect the organoleptic properties of food products, as well as efficient encapsulation techniques, are necessary for its implementation in global agriculture.

Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method

Near-infrared (NIR) hyperspectroscopy becomes an emerging nondestructive sensing technology for inspection of crop seeds. A large spectral dataset of more than 140,000 wheat kernels in 30 varieties was prepared for classification. Feature selection is a critical segment in large spectral data analysis. A novel convolutional neural network-based feature selector (CNN-FS) was proposed to screen out deeply target-related spectral channels. A convolutional neural network with attention (CNN-ATT) framework was designed for one-dimension data classification. Popular machine learning models including support vector machine (SVM) and partial least square discrimination analysis were used as the benchmark classifiers. Features selected by conventional feature selection algorithms were considered for comparison. Results showed that the designed CNN-ATT produced a higher performance than the compared classifier. The proposed CNN-FS found a subset of features, which made a better representation of raw dataset than conventional selectors did. The CNN-ATT achieved an accuracy of 93.01% using the full spectra and keep its high precision (90.20%) by training on the 60-channel features obtained via the CNN-FS method. The proposed methods have great potential for handling the analyzing tasks on other large spectral datasets. The proposed feature selection structure can be extended to design other new model-based selectors. The combination of NIR hyperspectroscopic technology and the proposed models has great potential for automatic nondestructive classification of single wheat kernels.

A Raman spectroscopy analysis method for rapidly determining saccharides and its application to monitoring the extraction process of Wenxin granule manufacturing procedure

Saccharides are the major constituents of many herbs, and they are often utilized as quality indicators of many botanical drugs, such as Chinese medicines. A method for the rapid determination of saccharides in the in-process extract solutions is beneficial for process monitoring and ensuring consistency in the quality of the end-products during the manufacturing of Chinese medicines. In this work, a method based on Raman spectroscopy and a competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model was established for the rapid quantification of saccharides. The accuracy and precision of this method were confirmed by employing one monosaccharide (glucose), one oligosaccharide (maltotriose), and two polysaccharides (Codonopsis radix polysaccharides and Polygonati rhizome polysaccharides) as reference substances. The determined results correlated well with the reference values of the four substances with the coefficient of determination of prediction (R_p²) ≥ 0.9939 and the root-mean-square error of prediction (RMSEP) ≤ 1.1052 mg/mL. Then, the method was applied to monitoring the simulated extraction process for Wenxin granule manufacture using total saccharides as a quality indicator. The CARS-PLS model exhibited satisfactory fitting and predictive capability, with R_p² and RMSEP values of 0.9743 and 1.4931 mg/mL, respectively. Our work demonstrated that Raman spectroscopy coupled with chemometrics can offer a reliable and nondestructive alternative for the determination of different types of saccharides, in addition to being useful for real-time monitoring of the extraction process during the manufacturing of Wenxin granules. The presented approach is expected to be applicable to other Chinese medicines.

Feasibility of NIR spectroscopy detection of moisture content in coco-peat substrate based on the optimization characteristic variables

Moisture content is an important index to evaluate the water content in substrate. Near-infrared (NIR) spectroscopy was used for rapid quantitative detection of moisture content of coco-peat substrate. The different spectral pretreatment methods were adopted to pre-process the spectral data. Successive projection algorithm (SPA), elimination of uninformative variables algorithm (UVE) and synergy interval partial least squares algorithm (Si-PLS) were used to screen characteristic variables of coco-peat substrate original spectral data and different pretreatment spectral data. The partial least squares (PLSR) and multiple linear regression (MLR) were used to establish the relationship model between the spectral data and reference measurement value of moisture content. In comparison, the best and simplest spectral prediction model was established when SPA was used to screen the characteristic variables of Savitzky-Golay (S-G) smoothing spectral data and MLR was used to establish the model. And the corresponding correlation coefficient and root mean square error of calibration set were 0.9976 and 1.0989%, respectively; the correlation coefficient and root mean square error of prediction set were 0.9963 and 1.4029%, respectively, and RPD was 11.28. The results of this study provided a feasible method for the rapid detection of moisture content of coco-peat substrate.