Shape induced reflectance correction for non-destructive determination and visualization of soluble solids content in winter jujubes using hyperspectral imaging in two different spectral ranges

Abstract Soluble solids content (SSC) is an important quality attribute in determining fruit maturity and grading after harvest. This study explored the potential of hyperspectral imaging (HSI) coupled with multivariate analysis in visible and near-infrared (Vis-NIR, 380−1030 nm) and near-infrared (NIR, 874−1734 nm) regions for measuring SSC in winter jujube fruit. The effectiveness of applying area normalization to reduce the influence of non-uniform light distribution on the spherical surface of intact fruit was explored. Then linear and non-linear regression models were developed and compared in two spectral ranges. The performance obtained by the least squares-support vector machine (LS-SVM) models based on successive projection algorithm (SPA) was satisfactory. The determination coefficient of prediction (Rp2) and residual predictive deviation (RPD) were 0.873 and 2.81 for the NIR range, and 0.894 and 3.07 for the Vis-NIR range, respectively. The SPA-LSSVM models were applied on the pixel-wise and object-wise spectra of region of interest before and after area normalization for comparison of visualization performance of corresponding prediction maps for SSC. Area normalization could effectively correct the non-uniform reflectance on a spherical object. The overall results indicated that HSI could be used to non-destructively predict and visualize SSC in winter jujubes.

[1]  Y. Tao.  Spherical transform of fruit images for on‐line defect extraction of mass objects , 1996 .

[2]  Peter Goos,et al.  Glare based apple sorting and iterative algorithm for bruise region detection using shortwave infrared hyperspectral imaging , 2017 .

[3]  He Jianguo,et al.  Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging , 2016 .

[4]  N. Lallu,et al.  Soluble solids accumulation and postharvest performance of ‘Hayward’ kiwifruit , 2013 .

[5]  Te Ma,et al.  Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging , 2018 .

[6]  Umezuruike Linus Opara,et al.  Analytical methods for determination of sugars and sweetness of horticultural products—A review , 2015 .

[7]  Pengcheng Nie,et al.  Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration , 2013, Food and Bioprocess Technology.

[8]  Ingo Truppel,et al.  An approach to non-destructive apple fruit chlorophyll determination , 2002 .

[9]  Chu Zhang,et al.  Detection of Subtle Bruises on Winter Jujube Using Hyperspectral Imaging With Pixel-Wise Deep Learning Method , 2019, IEEE Access.

[10]  Shintaroh Ohashi,et al.  Nondestructive evaluation of jujube quality by visible and near-infrared spectroscopy , 2011 .

[11]  R. Lu.  PREDICTING FIRMNESS AND SUGAR CONTENT OF SWEET CHERRIES USING NEAR–INFRARED DIFFUSE REFLECTANCE SPECTROSCOPY , 2001 .

[12]  Yong He,et al.  Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging. , 2018, Food chemistry.

[13]  Mantong Zhao,et al.  Pears characteristics (soluble solids content and firmness prediction, varieties) testing methods based on visible-near infrared hyperspectral imaging , 2016 .

[14]  Haiyan Cen,et al.  Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification , 2016 .

[15]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[16]  Fan Zhao,et al.  Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging , 2015, Food Analytical Methods.

[17]  G. Mazza,et al.  Rapid determination of carbohydrates, ash, and extractives contents of straw using attenuated total reflectance fourier transform mid-infrared spectroscopy. , 2011, Journal of agricultural and food chemistry.

[18]  Jiangbo Li,et al.  Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods , 2013 .

[19]  Boqiang Li,et al.  Inhibitory of grey mold on green pepper and winter jujube by chlorine dioxide (ClO2) fumigation and its mechanisms , 2019, LWT.

[20]  Alessandro Ulrici,et al.  Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee. , 2017, Analytica chimica acta.

[21]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[22]  J. S. Ribeiro,et al.  Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy. , 2011, Talanta.

[23]  Li Feng,et al.  Grading and Sorting of Grape Berries Using Visible-Near Infrared Spectroscopy on the Basis of Multiple Inner Quality Parameters , 2019, Sensors.

[24]  Chu Zhang,et al.  Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans , 2016, Food and Bioprocess Technology.

[25]  Yuan-Yuan Pu,et al.  Vis-NIR hyperspectral imaging in visualizing moisture distribution of mango slices during microwave-vacuum drying. , 2015, Food chemistry.

[26]  Jun-Hu Cheng,et al.  Suitability of hyperspectral imaging for rapid evaluation of thiobarbituric acid (TBA) value in grass carp (Ctenopharyngodon idella) fillet. , 2015, Food chemistry.

[27]  Paul J. Williams,et al.  Near infrared hyperspectral imaging in quality and safety evaluation of cereals , 2018, Critical reviews in food science and nutrition.

[28]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[29]  Chu Zhang,et al.  Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis , 2018, Scientific Reports.

[30]  K. M. Wright,et al.  NMR protocol for on-line Brix determination , 2006 .

[31]  Yong He,et al.  Hyperspectral reflectance imaging combined with carbohydrate metabolism analysis for diagnosis of citrus Huanglongbing in different seasons and cultivars , 2018, Sensors and Actuators B: Chemical.

[32]  Di Wu,et al.  Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications , 2013 .

[33]  Di Wu,et al.  Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging , 2018 .

[34]  Da-Wen Sun,et al.  Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. , 2019, Annual review of food science and technology.

[35]  Da-Wen Sun,et al.  Potential of hyperspectral imaging for visual authentication of sliced organic potatoes from potato and sweet potato tubers and rapid grading of the tubers according to moisture proportion , 2016, Comput. Electron. Agric..

[36]  M. Kim,et al.  Determination of the total volatile basic nitrogen (TVB-N) content in pork meat using hyperspectral fluorescence imaging , 2018 .

[37]  Jifeng Ning,et al.  Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging. , 2015, Food chemistry.

[38]  Di Wu,et al.  Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging , 2013 .

[39]  Yong He,et al.  Comparison of different CCD detectors and chemometrics for predicting total anthocyanin content and antioxidant activity of mulberry fruit using visible and near infrared hyperspectral imaging technique. , 2017, Food chemistry.

[40]  Santosh Shrestha,et al.  Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis , 2016 .