Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use

Abstract The application of visible (Vis; 400–750 nm) and near infrared red (NIR; 750–2500 nm) region spectroscopy to assess fruit and vegetables is reviewed in context of ‘point’ spectroscopy, as opposed to multi- or hyperspectral imaging. Vis spectroscopy targets colour assessment and pigment analysis, while NIR spectroscopy has been applied to assessment of macro constituents (principally water) in fresh produce in commercial practice, and a wide range of attributes in the scientific literature. This review focusses to key issues relevant to the widespread implementation of Vis-NIR technology in the fruit sector. A background to the concepts and technology involved in the use of Vis-NIR spectroscopy is provided and instrumentation for in-field and in-line applications, which has been available for two and three decades, respectively, is described. A review of scientific effort is made for the period 2015 - February 2020, in terms of the application areas, instrumentation, chemometric methods and validation procedures, and this work is critiqued through comparison to techniques in commercial use, with focus to wavelength region, optical geometry, experimental design, and validation procedures. Recommendations for future research activity in this area are made, e.g., application development with consideration of the distribution of the attribute of interest in the product and the matching of optically sampled and reference method sampled volume; instrumentation comparisons with consideration of repeatability, optimum optical geometry and wavelength range). Recommendations are also made for reporting requirements, viz. description of the application, the reference method, the composition of calibration and test populations, chemometric reporting and benchmarking to a known instrument/method, with the aim of maximising useful conclusions from the extensive work being done around the world.

[1]  Charles Poynton,et al.  Frequently Asked Questions about Color , 1997 .

[2]  G. P Krivoshiev,et al.  A Possibility for Elimination of the Interference from the Peel in Nondestructive Determination of the Internal Quality of Fruit and Vegetables by VIS/NIR Spectroscopy , 2000 .

[3]  Pictiaw Chen,et al.  USE OF OPTICAL PROPERTIES OF FOOD MATERIALS IN QUALITY EVALUATION AND MATERIALS SORTING , 1978 .

[4]  Lembe S. Magwaza,et al.  A Review of Destructive and Non-destructive Methods for Determining Avocado Fruit Maturity , 2015, Food and Bioprocess Technology.

[5]  Umezuruike Linus Opara,et al.  Non-destructive prediction of internal and external quality attributes of fruit with thick rind: A review , 2018 .

[6]  Pierre Dardenne,et al.  Some Considerations about NIR Spectroscopy: Closing Speech at NIR-2009 , 2010 .

[7]  Francine Heisel,et al.  Detection of vegetation stress via a new high resolution fluorescence imaging system , 1996 .

[8]  Kerry B. Walsh,et al.  Spectrophotometer Ageing and Prediction of Fruit Attributes , 2016 .

[9]  Bernd Herold,et al.  Comparative study on maturity prediction in 'Elstar' and 'Jonagold' apples. , 2000 .

[10]  M. Jimenez-Cuesta,et al.  Determination of a color index for citrus fruit degreening , 1983 .

[11]  Wouter Saeys,et al.  Measurement of optical properties of fruits and vegetables: A review , 2020 .

[12]  José Blasco,et al.  A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy , 2016 .

[13]  Hui Yan,et al.  Hand-held near-infrared spectrometers: State-of-the-art instrumentation and practical applications , 2018, NIR news.

[14]  Kang Tu,et al.  Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy , 2015 .

[15]  M. Bawendi,et al.  A colloidal quantum dot spectrometer , 2015, Nature.

[16]  A. Gitelson,et al.  Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening , 1999 .

[17]  Yibin Ying,et al.  Applications of Near-Infrared Systems for Quality Evaluation of Fruits: A Review , 2016 .

[18]  Stefan Hörtensteiner,et al.  The biochemistry and molecular biology of chlorophyll breakdown , 2018, Journal of experimental botany.

[19]  Sumio Kawano.  Past, Present and Future near Infrared Spectroscopy Applications for Fruit and Vegetables , 2016 .

[20]  Robert G. Dambergs,et al.  A Review of the State of the Art, Limitations, and Perspectives of Infrared Spectroscopy for the Analysis of Wine Grapes, Must, and Grapevine Tissue , 2015 .

[21]  Ze’ev Schmilovitch,et al.  Forecast of Apple Internal Quality Indices at Harvest and During Storage by VIS-NIR Spectroscopy , 2014, Food and Bioprocess Technology.

[22]  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.

[23]  G. Casadoro,et al.  A new index based on vis spectroscopy to characterize the progression of ripening in peach fruit , 2008 .

[24]  A. Peirs,et al.  Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment , 2000 .

[25]  Kerry B. Walsh,et al.  Robustness of Partial Least-Squares Models to Change in Sample Temperature: II. Application to Fruit Attributes , 2014 .

[26]  John R. Miller,et al.  Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[27]  慶太 杉山,et al.  普通種ニンジンと金時ニンジンのα-，β-カロテンおよびリコペンの簡易分別定量法 , 2007 .

[28]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[29]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[30]  Yibin Ying,et al.  Factors influencing near infrared spectroscopy analysis of agro-products: a review , 2019, Frontiers of Agricultural Science and Engineering.

[31]  Anupun Terdwongworakul,et al.  Non-destructive classification of durian maturity of ‘Monthong’ cultivar by means of visible spectroscopy of the spine , 2012 .

[32]  Bart Nicolai,et al.  Estimation of the prior storage period of lamb’s lettuce based on visible/near infrared reflectance spectroscopy , 2016 .

[33]  M. Siddiqui,et al.  Inside and Beyond Color: Comparative Overview of Functional Quality of Tomato and Watermelon Fruits , 2019, Front. Plant Sci..

[34]  Alessandro Torricelli,et al.  Optical properties of developing pip and stone fruit reveal underlying structural changes. , 2015, Physiologia plantarum.

[35]  Alessandro Torricelli,et al.  Optical properties, ethylene production and softening in mango fruit , 2015 .

[36]  Itzhak Shmulevich,et al.  NIRS Detection of Moldy Core in Apples , 2010 .

[37]  A. Wellburn,et al.  The spectral determination of chlorophyll a and chlorophyll b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. , 1994 .

[38]  Kerry B. Walsh,et al.  Prediction of Brix Values of Intact Peaches with Least Squares-Support Vector Machine Regression Models , 2012 .

[39]  Sajad Majeed Zargar,et al.  Peel colour in apple (Malus × domestica Borkh.): An economic quality parameter in fruit market , 2019, Scientia Horticulturae.

[40]  Richard G. Leffler,et al.  Near Infrared Analysis of Soluble Solids in Intact Cantaloupe , 1989 .

[41]  Kerry B. Walsh,et al.  Evaluation of a Dry Extract System Involving NIR Spectroscopy (DESIR) for Rapid Assessment of Pesticide Contamination of Fruit Surfaces , 2012 .

[42]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[43]  C. Greensill,et al.  Sorting of Fruit Using near Infrared Spectroscopy: Application to a Range of Fruit and Vegetables for Soluble Solids and Dry Matter Content , 2004 .

[44]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[45]  Pankaj B. Pathare,et al.  Colour Measurement and Analysis in Fresh and Processed Foods: A Review , 2012, Food and Bioprocess Technology.

[46]  Ahmed M. Rady,et al.  Evaluation of Carrot Quality Using Visible-Near Infrared Spectroscopy and Multivariate Analysis , 2018 .

[47]  Charles R. Hurburgh,et al.  Improving the transfer of near infrared prediction models by orthogonal methods , 2009 .

[48]  Pablo J. Zarco-Tejada,et al.  Assessing structural effects on PRI for stress detection in conifer forests , 2011 .

[49]  A. Peirs,et al.  Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .

[50]  Q. Ling,et al.  Differentiation of chromoplasts and other plastids in plants , 2019, Plant Cell Reports.

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

[52]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[53]  Phil Williams,et al.  Tutorial: Items to be included in a report on a near infrared spectroscopy project , 2017 .

[54]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .

[55]  Harpreet Kaur,et al.  Comparison of hand-held near infrared spectrophotometers for fruit dry matter assessment , 2017 .

[56]  Sumio Kawano,et al.  Non-Destructive NIR Quality Evaluation of Fruits and Vegetables in Japan , 1994 .

[57]  C. Kumaravelu,et al.  A review on the applications of Near-Infrared spectrometer and Chemometrics for the agro-food processing industries , 2015, 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR).

[58]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[59]  Qin Ouyang,et al.  Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy , 2020 .

[60]  K. Norris,et al.  4. Direct Spectrophotometric Determination of Moisture Content of Grain and Seeds , 1996 .

[61]  José Blasco,et al.  Prediction of the level of astringency in persimmon using visible and near-infrared spectroscopy , 2017 .

[62]  I. Iglesias,et al.  Fruit color development, anthocyanin content, standard quality, volatile compound emissions and consumer acceptability of several ‘Fuji’ apple strains , 2012 .

[63]  S. Kawano,et al.  Determination of Sugar Content in Intact Peaches by Near Infrared Spectroscopy with Fiber Optics in Interactance Mode , 1992 .

[64]  Wouter Saeys,et al.  Spatially resolved spectroscopy for nondestructive quality measurements of Braeburn apples cultivated in sub-fertilization condition , 2013, Other Conferences.

[65]  R. Lu,et al.  Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique , 2008 .

[66]  David Ruiz,et al.  Application of reflectance colorimeter measurements and infrared spectroscopy methods to rapid and nondestructive evaluation of carotenoids content in apricot (Prunus armeniaca L.). , 2008, Journal of agricultural and food chemistry.

[67]  R. J. Blakey,et al.  Evaluation of avocado fruit maturity with a portable near-infrared spectrometer , 2016 .

[68]  Rafael Font,et al.  Application of visible/near-infrared reflectance spectroscopy for predicting internal and external quality in pepper. , 2016, Journal of the science of food and agriculture.

[69]  Michael Knee,et al.  Methods of measuring green colour and chlorophyll content of apple fruit , 2007 .

[70]  Danilo Monarca,et al.  Review: Recent Advances in the Use of Non-Destructive near Infrared Spectroscopy for Intact Olive Fruits , 2015 .

[71]  Jun-Hu Cheng,et al.  Recent Advances in Nondestructive Analytical Techniques for Determining the Total Soluble Solids in Fruits: A Review. , 2016, Comprehensive reviews in food science and food safety.

[72]  Rafael Aleixandre-Benavent,et al.  Bibliometric insights into the spectroscopy research field: A food science and technology case study , 2020 .

[73]  Ze’ev Schmilovitch,et al.  Non-destructive measurement of ascorbic acid content in bell peppers by VIS-NIR and SWIR spectrometry , 2012 .

[74]  José Blasco,et al.  Visible–NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit , 2015 .

[75]  Kerry B. Walsh,et al.  Manipulation of mango fruit dry matter content to improve eating quality , 2017 .

[76]  A. Gitelson,et al.  Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit , 2003 .

[77]  José Blasco,et al.  Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review , 2019, Trends in Food Science & Technology.

[78]  K. Walsh,et al.  Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy , 2006 .

[79]  Rui Guerra,et al.  A TSS classification study of ‘Rocha’ pear (Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra , 2017 .

[80]  S. Cubero,et al.  Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality , 2017, Food and Bioprocess Technology.

[81]  Alessandro Torricelli,et al.  Time-Resolved Reflectance Spectroscopy Applied to the Nondestructive Monitoring of the Internal Optical Properties in Apples , 2001 .

[82]  Kerry B. Walsh,et al.  The uses of near infra-red spectroscopy in postharvest decision support: A review , 2020 .

[83]  Manuela Zude,et al.  Spectral shift as advanced index for fruit chlorophyll breakdown , 2014, Food and Bioprocess Technology.

[84]  K. Walsh,et al.  Temporal and Environmental Sensitivity of a Photodiode Array Spectrophometric System , 2014 .

[85]  Kerry B. Walsh,et al.  Assessment of Titratable Acidity in Fruit Using Short Wave near Infrared Spectroscopy. Part B: Intact Fruit Studies , 2012 .

[86]  Xudong Sun,et al.  NIRS prediction of dry matter content of single olive fruit with consideration of variable sorting for normalisation pre-treatment , 2020 .

[87]  Hailong Wang,et al.  Fruit Quality Evaluation Using Spectroscopy Technology: A Review , 2015, Sensors.

[88]  M. Zude.  Comparison of indices and multivariate models to non-destructively predict the fruit chlorophyll by means of visible spectrometry in apple fruit , 2003 .

[89]  A. Peirs,et al.  Effect of biological variability on the robustness of NIR models for soluble solids content of apples , 2003 .

[90]  K. Walsh,et al.  A remote acceptance probe and illumination configuration for spectral assessment of internal attributes of intact fruit , 2000 .

[91]  T. Cardi,et al.  Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges , 2018 .

[92]  Manuela Zude,et al.  Spectrophotometric analyses of chlorophyll and single carotenoids during fruit development of tomato (Solanum lycopersicum L.) by means of iterative multiple linear regression analysis. , 2008, Applied optics.

[93]  Josse De Baerdemaeker,et al.  Spatially resolved diffuse reflectance in the visible and near-infrared wavelength range for non-destructive quality assessment of ‘Braeburn’ apples , 2014 .

[94]  Bart Nicolai,et al.  Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: A review , 2019 .

[95]  Lourdes Salguero-Chaparro,et al.  On-line versus off-line NIRS analysis of intact olives , 2014 .

[96]  G. S. Birth,et al.  Nondestructive Spectrophotometric Determination of Dry Matter in Onions , 1985, Journal of the American Society for Horticultural Science.

[97]  Bahareh Jamshidi,et al.  Ability of near-infrared spectroscopy for non-destructive detection of internal insect infestation in fruits: Meta-analysis of spectral ranges and optical measurement modes. , 2020, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[98]  J. Pierna,et al.  Soil parameter quantification by NIRS as a Chemometric challenge at ‘Chimiométrie 2006’ , 2008 .

[99]  A. Gitelson,et al.  Remote estimation of chlorophyll content in higher plant leaves , 1997 .

[100]  Kerry B. Walsh,et al.  Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy , 2011 .

[101]  Jan Kuckenberg,et al.  Evaluation of fluorescence and remission techniques for monitoring changes in peel chlorophyll and internal fruit characteristics in sunlit and shaded sides of apple fruit during shelf-life , 2008 .

[102]  Kerry B. Walsh,et al.  Assessment of Titratable Acidity in Fruit Using Short Wave near Infrared Spectroscopy. Part A: Establishing a Detection Limit Based on Model Solutions , 2012 .

[103]  Kerry B. Walsh,et al.  Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy , 2016 .

[104]  A. Wellburn.  The Spectral Determination of Chlorophylls a and b, as well as Total Carotenoids, Using Various Solvents with Spectrophotometers of Different Resolution* , 1994 .

[105]  Simó Alegre,et al.  The Effects of Reflective Film on Fruit Color, Quality, Canopy Light Distribution, and Profitability of ‘Mondial Gala’ Apples , 2009 .

[106]  G. A. Blackburn,et al.  Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves , 1998 .

[107]  T. Cattaneo,et al.  Review: NIR Spectroscopy as a Suitable Tool for the Investigation of the Horticultural Field , 2019, Agronomy.

[108]  Davide Contini,et al.  Recent Advances in Time-resolved Nir Spectroscopy for Nondestructive Assessment of Fruit Quality , 2015 .

[109]  K. Walsh,et al.  Short-Wavelength Near-Infrared Spectra of Sucrose, Glucose, and Fructose with Respect to Sugar Concentration and Temperature , 2003, Applied spectroscopy.

[110]  Siwalak Pathaveerat,et al.  Nondestructive determination of dry matter and soluble solids content in dehydrator onions and garlic using a handheld visible and near infrared instrument , 2017 .

[111]  Satyam Srivastava,et al.  Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review , 2018, Journal of Food Measurement and Characterization.

[112]  Shirley Anderson,et al.  Determination of fat, moisture, and protein in meat and meat products by using the FOSS FoodScan Near-Infrared Spectrophotometer with FOSS Artificial Neural Network Calibration Model and Associated Database: collaborative study. , 2007, Journal of AOAC International.

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

[114]  Ruoyu Zhang,et al.  Optimization of soluble solids content prediction models in ‘Hami’ melons by means of Vis-NIR spectroscopy and chemometric tools , 2019, Infrared Physics & Technology.

[115]  Jean Michel Roger,et al.  Orthogonal Projections in the Row and the Column Spaces , 2016 .

[116]  Xudong Sun,et al.  Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content , 2020 .

[117]  Kang Tu,et al.  Quality assessment and discrimination of intact white and red grapes from Vitis vinifera L. at five ripening stages by visible and near-infrared spectroscopy , 2018 .

[118]  K. Peiris,et al.  Spatial variability of soluble solids or dry-matter content within individual fruits, bulbs, or tubers : Implications for the development and use of NIR spectrometric techniques , 1999 .

[119]  José Blasco,et al.  Integration of simultaneous tactile sensing and reflectance visible and near-infrared spectroscopy in a robot gripper for mango quality assessment , 2017 .

[120]  Xudong Sun,et al.  Simultaneous measurement of brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy , 2016 .

[121]  Colin Victor. Greensill,et al.  Improving Calibration Transfer between Shortwave near Infrared Silicon Photodiode Array Instruments , 2016 .

[122]  Kerry B. Walsh,et al.  Robustness of Partial Least-Squares Models to Change in Sample Temperature: I. A Comparison of Methods for Sucrose in Aqueous Solution , 2014 .

[123]  V. A. McGlone,et al.  Dry matter determination in ‘Hass’ avocado by NIR spectroscopy , 2003 .

[124]  Bo Li,et al.  Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review , 2017, Plants.

[125]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .

[126]  P. Schaare,et al.  Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis) , 2000 .

[127]  B. Nicolai,et al.  Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear , 2008 .

[128]  V. Mcglone,et al.  Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy , 2003 .

[129]  K. Walsh,et al.  Robustness of Tomato Quality Evaluation Using a Portable Vis-SWNIRS for Dry Matter and Colour , 2017, International journal of analytical chemistry.

[130]  R. Cubeddu,et al.  In vivo absorption and scattering spectroscopy of biological tissues , 2003, Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology.