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      Fruit Quality Evaluation Using Spectroscopy Technology: A Review

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          Abstract

          An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified.

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          PLS-regression: a basic tool of chemometrics

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            Performance of some variable selection methods when multicollinearity is present

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              Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration.

              By employing the simple but effective principle 'survival of the fittest' on which Darwin's Evolution Theory is based, a novel strategy for selecting an optimal combination of key wavelengths of multi-component spectral data, named competitive adaptive reweighted sampling (CARS), is developed. Key wavelengths are defined as the wavelengths with large absolute coefficients in a multivariate linear regression model, such as partial least squares (PLS). In the present work, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each wavelength. Then, based on the importance level of each wavelength, CARS sequentially selects N subsets of wavelengths from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a calibration model. Next, based on the regression coefficients, a two-step procedure including exponentially decreasing function (EDF) based enforced wavelength selection and adaptive reweighted sampling (ARS) based competitive wavelength selection is adopted to select the key wavelengths. Finally, cross validation (CV) is applied to choose the subset with the lowest root mean square error of CV (RMSECV). The performance of the proposed procedure is evaluated using one simulated dataset together with one near infrared dataset of two properties. The results reveal an outstanding characteristic of CARS that it can usually locate an optimal combination of some key wavelengths which are interpretable to the chemical property of interest. Additionally, our study shows that better prediction is obtained by CARS when compared to full spectrum PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE) and moving window partial least squares regression (MWPLSR).
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                21 May 2015
                May 2015
                : 15
                : 5
                : 11889-11927
                Affiliations
                College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; E-Mails: hl_wang@ 123456zju.edu.cn (H.W.); jypeng@ 123456zju.edu.cn (J.P.); cqxie@ 123456zju.edu.cn (C.X.)
                Author notes
                [* ]Authors to whom correspondence should be addressed; E-Mails: ydbao@ 123456zju.edu.cn (Y.B.); yhe@ 123456zju.edu.cn (Y.H.); Tel./Fax: +86-571-8898-2143 (Y.H.).
                Article
                sensors-15-11889
                10.3390/s150511889
                4481958
                26007736
                6e0ccd90-fee4-438e-b878-58a2b9b52092
                © 2015 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 17 March 2015
                : 18 May 2015
                Categories
                Review

                Biomedical engineering
                vis/nir,fruit quality,chemometrics,discrimination,characteristic wavelength,ssc,acidity

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