No abstract

No abstract

The performance of a support vector regression (SVR) model with a Gaussian radial basis kernel to predict anthocyanin concentration, pH index and sugar content in whole grape berries, using spectroscopic measurements obtained in reflectance mode, was evaluated. Each sample contained a small number of whole berries and the spectrum of each sample was collected during ripening using hyperspectral imaging in the range of 380-1028 nm. Touriga Franca (TF) variety samples were collected for the 2012-2015 vintages, and Touriga Nacional (TN) and Tinta Barroca (TB) variety samples were collected for the 2013 vintage. These TF vintages were independently used to train, validate and test the SVR methodology; different combinations of TF vintages were used to train and test each model to assess the performance differences under wider and more variable datasets; the varieties that were not employed in the model training and validation (TB and TN) were used to test the generalization ability of the SVR approach. Each case was tested using an external independent set (with data not included in the model training or validation steps). The best R 2 results obtained with varieties and vintages not employed in the model's training step were 0.89, 0.81 and 0.90, with RMSE values of 35.6 mg•L −1 , 0.25 and 3.19 • Brix, for anthocyanin concentration, pH index and sugar content, respectively. The present results indicate a good overall performance for all cases, improving the state-of-the-art results for external test sets, and suggesting that a robust model, with a generalization capacity over different varieties and harvest years may be obtainable without further training, which makes this a very competitive approach when compared to the models from other authors, since it makes the problem significantly simpler and more cost-effective.

A significant number of batch process monitoring methods have been proposed since the first groundbreaking approaches were published in the literature, two decades ago. The proper assessment of all the alternatives currently available requires a rigorous and robust assessment framework, in order to assist practitioners in their difficult task of selecting the most adequate approach for the particular situations they face and in the definition of all the optional aspects required, such as the type of preprocessing, infilling, alignment, etc. However, comparison methods currently adopted present several limitations and even some flaws that make the variety of studies available not easily generalizable or, in extreme situations, fundamentally wrong. Without a proper comparison tool decisions are made on a subjective basis and therefore are prone to be at least suboptimal. In this article we present a structured review of comparison methods and figures of merit adopted to assess batch process monitoring approaches, analyzing their strengths and limitations, as well as some common missuses. Furthermore, we propose a comparison and assessment methodology (CAM) and figures of merit that should be adopted in order to circumvent some of the limitations of the current procedures. We begin by addressing, in this article, the analysis of the methods' "detection strength". Detection strength regards the ability to correctly detect abnormal situations without resulting in excessive false alarms. We describe in detail how the comparison framework is implemented and illustrate its application in two case studies, encompassing a rich variety of testing scenarios (99 000 batch runs) and monitoring methods (2-way, 3-way, and dynamic methods, amounting to a total of 60 different combinations of methods and their variants). From this analysis, it stands out that the CAM methodology is suitable for comparing different monitoring methods, even when the number of methods and variants is very large. It provides simple, informative, and statisticalbased metrics to assess a given method's performance and is able to quantify the added value of alternative approaches. When applied to the two case studies considered, 2-way methods (with batch-wise unfolding) combined with missing data (MD) infilling and dynamic time warping (DTW) were found to provide adequate solutions. If a more parsimonious model is required, dynamic methods such as autoregressive principal components analysis (ARPCA) provide good alternatives and Tucker3 with current deviations (CD) infilling also presents an interesting performance for some fault scenarios. In general, no method can claim absolute supremacy in all faulty scenarios.

Remote sensing technology, such as hyperspectral imaging, in combination with machine learning algorithms, has emerged as a viable tool for rapid and nondestructive assessment of wine grape ripeness. However, the differences in terroir, together with the climatic variations and the variability exhibited by different grape varieties, have a considerable impact on the grape ripening stages within a vintage and between vintages and, consequently, on the robustness of the predictive models. To address this challenge, we present a novel one-dimensional convolutional neural network architecture-based model for the prediction of sugar content and pH, using reflectance hyperspectral data from different vintages. We aimed to evaluate the model’s generalization capacity for different varieties and for a different vintage not employed in the training process, using independent test sets. A transfer learning mechanism, based on the proposed convolutional neural network, was also used to evaluate improvements in the model’s generalization. Overall, the results for generalization ability showed a very good performance with RMSEP values of 1.118 °Brix and 1.085 °Brix for sugar content and 0.199 and 0.183 for pH, for test sets using different varieties and a different vintage, respectively, improving and updating the current state of the art.