A non-destructive methodology for determination of cantaloupe sugar content using machine vision and deep learning.

BACKGROUND: To determine the maturity of cantaloupe, measuring the soluble solid content (SSC) as the indicator of sugar content based on the refractometric index is commonly practised. This method, however, is destructive and limited to a small number of samples. In this study, the coupling of a convolutional neural network (CNN) with machine vision was proposed in detecting the SSC of cantaloupe. The cantaloupe images were first acquired under controlled and uncontrolled conditions and subsequently fed to the CNN to predict the class to which each cantaloupe belonged. Four hand-crafted classical machine-learning classifiers were used to compare against the performance of the CNN. RESULTS: Experimental results showed that the CNN method significantly outperformed others, with an improvement of >100% being achieved in terms of classification accuracy, considering the data acquired under the uncontrolled environment. CONCLUSION: The results demonstrated the potential benefit to operationalize CNNs in practice for SSC determination of cantaloupe before harvesting. © 2022 Society of Chemical Industry.

Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm.

In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R2) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R2 values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.

Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics.

Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400-2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents' specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models' identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.

Terahertz sensing of chlorpyrifos-methyl using metamaterials.

By squeezing electromagnetic energy into small volumes near a metal-dielectric interface, plasmonics provide many routes to enhance and manipulate light-matter interactions, which presents new strategies for signal enhancing technologies. As an extension of the ideas of plasmonics to the terahertz (THz) range, metamaterials have shown great potential in sensing applications. In this study, terahertz time-domain spectroscopy (THz-TDS) combined with metamaterials was used to detect chlorpyrifos-methyl (CM), which is one type of the broad-spectrum organophosphorus pesticides. The results demonstrate that sensitivity is greatly improved using THz metamaterials, with the limit of detection (LOD) of CM reaching 0.204mgL-1, which is lower than the World Health Organization's provisional guideline limit for CM in vegetables (1mgL-1). The results indicated that THz spectroscopy combined with metamaterials could be a valuable method for highly sensitive THz applications, presenting a new strategy for food quality and safety control in the future.

Integration of independent component analysis with near-infrared spectroscopy for analysis of bioactive components in the medicinal plant Gentiana scabra Bunge.

Independent component (IC) analysis was applied to near-infrared spectroscopy for analysis of gentiopicroside and swertiamarin; the two bioactive components of Gentiana scabra Bunge. ICs that are highly correlated with the two bioactive components were selected for the analysis of tissue cultures, shoots and roots, which were found to distribute in three different positions within the domain [two-dimensional (2D) and 3D] constructed by the ICs. This setup could be used for quantitative determination of respective contents of gentiopicroside and swertiamarin within the plants. For gentiopicroside, the spectral calibration model based on the second derivative spectra produced the best effect in the wavelength ranges of 600-700 nm, 1600-1700 nm, and 2000-2300 nm (correlation coefficient of calibration = 0.847, standard error of calibration = 0.865%, and standard error of validation = 0.909%). For swertiamarin, a spectral calibration model based on the first derivative spectra produced the best effect in the wavelength ranges of 600-800 nm and 2200-2300 nm (correlation coefficient of calibration = 0.948, standard error of calibration = 0.168%, and standard error of validation = 0.216%). Both models showed a satisfactory predictability. This study successfully established qualitative and quantitative correlations for gentiopicroside and swertiamarin with near-infrared spectra, enabling rapid and accurate inspection on the bioactive components of G. scabra Bunge at different growth stages.

Optimization of suitable ethanol blend ratio for motorcycle engine using response surface method.

In view of energy shortage and air pollution, ethanol-gasoline blended fuel used for motorcycle engine was studied in this work. The emissions of carbon monoxide (CO), nitrogen oxides (NO(X)) and engine performance of a 125 cc four-stroke motorcycle engine with original carburetor using ethanol-gasoline fuels were investigated. The model of three-variable Box Behnken design (BBD) was used for experimental design, the ethanol blend ratios were prepared at 0, 10, 20 vol%; the speeds of motorcycle were selected as 30, 45, 60 km/h; and the throttle positions were set at 30, 60, 90 %. Both engine performance and air pollutant emissions were then analyzed by response surface method (RSM) to yield optimum operation parameters for tolerable pollutant emissions and maximum engine performance. The RSM optimization analysis indicated that the most suitable ethanol-gasoline blended ratio was found at the range of 3.92-4.12 vol% to yield a comparable fuel conversion efficiency, while considerable reductions of exhaust pollutant emissions of CO (-29 %) and NO(X) (-12 %) when compared to pure gasoline fuel. This study demonstrated low ethanol-gasoline blended fuels could be used in motorcycle carburetor engines without any modification to keep engine power while reducing exhaust pollutants.