Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange

Modeling and De-Noising for Nondestructive Detection of Total Soluble Solid Content of Pomelo by Using Visible/Near Infrared Spectroscopy

The flavor of Pomelo is highly variable and difficult to determine without peeling the fruit. The quality of pomelo flavor is due largely to the total soluble solid content (TSSC) in the fruit and there is a commercial need for a quick but nondestructive TSSC detection method for the industrial grading of pomelo. Due to the large size and thick mesocarp of pomelo, determining the internal quality of a pomelo fruit in a nondestructive manner is difficult, and the detection accuracy is further complicated by the noise typically generated by the common methods for the internal quality detection of other fruits. Thus, the aim of this study was to determine the optimal method to accurately detect pomelo TSSC and find a de-noising model which reduces the influence of noise on the optimal method's results. After developing a full-transmission visible/near infrared (VIS/NIR) spectroscopy sampling method, the confirming experimental results showed that the optimal pomelo TSSC detection model was Savitzky Golay + standard normal variate + competitive adaptive reweighted sampling + partial least squares regression. The R2 and RMSE of the calibration set for pomelo TSSC detection were 0.8097 and 0.8508, respectively, and the R2 and RMSE of the validation set for pomelo TSSC detection were 0.8053 and 0.8888, respectively. Both reference and dark de-noising are important for pomelo internal quality detection and should be calibrated frequently to compensate for time drift. This study found that large sensor response translation noise can be reduced with an artificial horizontal shift. Data supplementation is efficient for improving the adaption of the detection model for batch differences in pomelo samples. Using this optimized de-noising model to compensate for time drift, sensor response translation, and batch differences, the developed detection method is capable of satisfying the requirements of the industry (TSSC detection R2 was equal or larger than 0.9, RMSE was less than 1). These results indicate that full-transmission VIS/NIR spectroscopy can be exploited to realize the nondestructive detection of pomelo TSSC on an industrial scale, and that the methodologies used in this study can be immediately implemented in real-world production.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R²), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy

BACKGROUND

The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded.

RESULTS

The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively.

CONCLUSION

The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.

An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange

The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R² = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.