Non-Destructive Soluble Solids Content Determination for ‘Rocha’ Pear Based on VIS-SWNIR Spectroscopy under ‘Real World’ Sorting Facility Conditions

Abstract

In this paper we report a method to determine the soluble solids content (SSC) of ‘Rocha’ pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500–1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 ∘Brix and 1.09 ∘Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more ‘real world-like’ conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization.

Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy

“…This would allow a better understanding on the effective advances and contributions made by each study. Other models' metrics parameters, such as the prediction gain, may also be useful, as reported by [100]. Secondly, the calibration models' robustness must be addressed and solved through a stringent multi-year, multicultivar and multi-orchard validation, such as previously reported by [66].…”

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

“…For the time being, however, the majority of both experimental studies and practical applications, at least on Vis-NIRS, involve spectral "point" measuring systems instead of MHS imaging. They have been used to assess several plant and fruit quality attributes, disorders and pathologies [10,11,54,[78][79][80]. They have also been incorporated in many commercial applications to be used on inline, benchtop and handheld devices, which similarly to the MHS imaging systems, may benefit from the non-linear techniques of machine and deep learning to obtain the best calibration models.…”

“…Overall, for all sensors systems described above, the ultimate breakthrough is linked with today's explosive development of advanced and powerful machine learning methods of data processing, harnessing big data to infer critical information, such as, the classic partial least squares (PLS), support vector machines, artificial neural networks, classification techniques, deep learning, and other artificial intelligence (AI) approaches [60,65,68,70,81,[86][87][88][89]. This opens a number of novel perspectives in the assessment and classification, beyond the stateof-the-art, whose current landmarks can be represented by the following examples: the automated identification and classification of Chinese medicinal plants with different sensing techniques, including Vis-NIRS [90]; the prediction of quality attributes and internal browning disorder in "Rocha" pear by Vis-NIRS reflectance and semi-transmittance spectra taken under real-life conditions met in an automated inline grading system [79,80,91]; the assessment of citrus ripening on-tree [83]; the in situ grapevine identification (down to the level of varieties) via leaf reflectance spectra [92]; the anthocyanins fingerprinting in intact grape berries [93]; the detection of mercury induced stress in tobacco plants [94]. Additionally, it is worth mentioning the use of specific algorithms, as the hyperspectral insect damage detection algorithm (HIDDA), which allowed automatic detection of insect-damaged coffee beans using only a few bands and one hyperspectral signature [70]; or the RELIEF-F algorithm used to select the most discriminative features (wavelengths) and two band normalized differences for developing spectral disease indices for SCR detection and severity classification [65].…”

Nutritional Components of Beverage Granules by Near-Infrared Spectroscopy Based on PLS Model

“…To reduce costs and make huge profits, some businesses in the market replace milk or milk powder with cheap food fillers or addi-tives such as nondairy creamer and maltodextrin and rely on a large number of trans fatty acids to improve the taste and make the taste better. At the same time, the ambiguity of relevant industry standards makes some illegal businessmen "steal the concept" and give the title of traditional dairy products to milk tea powder solid beverage [2].…”