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

Acharya, 2012, Evaluation of a dry extract system involving NIR spectroscopy (DESIR) for rapid assessment of pesticide contamination of fruit surfaces, Am. J. of Anal. Chem., 3, 524, 10.4236/ajac.2012.38070 Acharya, 2014, Robustness of partial least squares models to change in sample temperature: I. A comparison of methods for sucrose in aqueous solutions, J. Near Infrared Spectrosc., 22, 279, 10.1255/jnirs.1113 Acharya, 2014, Robustness of partial least squares models to change in sample temperature: II. Application to fruit attributes, J. Near Infrared Spectrosc., 22, 287, 10.1255/jnirs.1119 Acharya, 2016, Spectrophotometer ageing and prediction of fruit attributes, J. Near Infrared Spectrosc., 24, 337, 10.1255/jnirs.1218 Acharya, 2017, Robustness of tomato quality evaluation using a portable vis-SWNIRS for dry matter and colour, Int. J. of Anal. Chem., 2863454 Aleixandre-Tudó, 2019, Bibliometric insights into the spectroscopy research field: a food science and technology case study, Appl. Spectrosc. Rev. Andersen, 2013, NIR spectrometer technology comparison Anderson, 2007, 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, J. AOAC International, 90, 1073, 10.1093/jaoac/90.4.1073 Anderson, 2017, Manipulation of mango fruit dry matter content to improve eating quality, Scientia Horticulturae, 226, 316, 10.1016/j.scienta.2017.09.001 Arendse, 2017, Non-destructive prediction of internal and external quality attributes of fruit with thick rind: a review, J. Food Eng., 217, 11, 10.1016/j.jfoodeng.2017.08.009 Australian Mango Industry Association, 2019. https://www.industry.mangoes.net.au/resource-collection/?tag=Objective+Reporting; d.o.a. 01/04/2019. Bao, 2015, A colloidal quantum dot spectrometer, Nature, 523, 67, 10.1038/nature14576 Bexiga, 2017, A TSS classification study of ‘Rocha’ pear (Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra, Postharvest Biol. and Tech., 132, 23, 10.1016/j.postharvbio.2017.05.014 Birth, 1985, Nondestructive spectrophotometric determination of dry matter in onions, J. Amer. Soc. Hort. Sci., 110, 297, 10.21273/JASHS.110.2.297 Blackburn, 1998, Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves, Int. J. of Remote Sensing, 19, 657, 10.1080/014311698215919 Blakey, 2016, Evaluation of avocado fruit maturity with a portable near-infrared spectrometer, Postharvest Biol. Technol., 121, 101, 10.1016/j.postharvbio.2016.06.016 Cattaneo, 2019, Review: NIR spectroscopy as a suitable tool for the investigation of the horticultural Field, Agronomy, 9, 503, 10.3390/agronomy9090503 Chen, 1978, Use of optical properties of food materials in quality evaluation and materials sorting, J. Food Proc. Eng., 2, 307, 10.1111/j.1745-4530.1978.tb00213.x Clark, 2003, Dry matter determination in ‘Hass’ avocado by NIR spectroscopy, Postharvest Biol. Technol., 29, 300, 10.1016/S0925-5214(03)00046-2 Cohen, 2008, 36 Cortés, 2016, A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy, Postharvest Biol. Tech., 118, 148, 10.1016/j.postharvbio.2016.04.011 Cortés, 2017, Integration of simultaneous tactile sensing and reflectance visible and near-infrared spectroscopy in a robot gripper for mango quality assessment, Biosystems Eng., 166, 112, 10.1016/j.biosystemseng.2017.08.005 Cortés, 2017, Sweet and non-sweet taste discrimination of nectarines using visible and near infrared spectroscopy, Postharvest Biol. Tech., 133, 113, 10.1016/j.postharvbio.2017.07.015 Cortés, 2017, Visible and near infrared diffuse reflectance spectroscopy for fast qualitative and quantitative assessment of nectarine quality, Food Bioprocess Technol., 10, 1755, 10.1007/s11947-017-1943-y Cortés, 2017, Prediction of the level of astringency in persimmon using visible and near-infrared spectroscopy, J. Food Eng., 204, 27, 10.1016/j.jfoodeng.2017.02.017 Cortés, 2019, Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: a review, Trends Food Sci. Technol., 85, 138, 10.1016/j.tifs.2019.01.015 Cubeddu, 2001, Time-resolved reflectance spectroscopy applied to the nondestructive monitoring of the internal optical properties in apples, Appl. Spectrosc., 55, 1368, 10.1366/0003702011953496 Dambergs, 2015, A review of the State of the art, limitations, and perspectives of infrared spectroscopy for the analysis of wine grapes, must, and grapevine tissue, Appl. Spectrosc. Rev., 50, 261, 10.1080/05704928.2014.966380 Dar, 2018, Peel colour in apple (malus x domestica borkh.): An economic quality parameter in fruit market, Scienta Horticulturae, 244, 50, 10.1016/j.scienta.2018.09.029 Dardenne, 2010, Some considerations about NIR spectroscopy: closing speech at NIR-2009, NIR News, 21, 8, 10.1255/nirn.1165 Dash, 2004, The MERIS terrestrial chlorophyll index, Int. J. Remote Sens., 25, 5403, 10.1080/0143116042000274015 De Jager, 1996, Prediction of optimum harvest date of jonagold, 94, 21 Dull, 1989, Near infrared analysis of soluble solids in intact cantaloupe, J. Food Sci., 54, 393, 10.1111/j.1365-2621.1989.tb03090.x Fraser, 2003, Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy, Postharvest Biol. Tech., 27, 185, 10.1016/S0925-5214(02)00058-3 Gamon, 1992, A narrow-wave band spectral index that tracks diurnal changes in photosynthetic efficiency, Remote Sens. Environ., 41, 35, 10.1016/0034-4257(92)90059-S Gitelson, 1997, Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll, Int. J. Remote Sens., 18, 2691, 10.1080/014311697217558 Golic, 2003, Short-wavelength near-infra-red spectra of sucrose, glucose and fructose with respect to sugar concentration and temperature, Appl. Spectrosc., 57, 139, 10.1366/000370203321535033 Greensill, 2000, Optimisation of instrument precision and wavelength resolution for the performance of NIR calibrations of sucrose in a water-cellulose matrix, Appl. Spectrosc., 41, 426, 10.1366/0003702001949528 Greensill, 2001, A remote acceptance probe and illumination configuration for spectral assessment of internal attributes of intact fruit, Meas. Sci. Technol., 11, 1674, 10.1088/0957-0233/11/12/304 Guo, 2020, Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy, J. Food Eng., 279 Haboudane, 2002, Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture, Remote Sens. Environ., 84, 416, 10.1016/S0034-4257(02)00018-4 Hayes, 2014, Temporal and environmental sensitivity of a photodiode array spectrophotometric system, J. Near Infrared Spectrosc., 22, 297, 10.1255/jnirs.1112 Hayes, 2016, Improving calibration transfer between shortwave near infrared silicon photodiode array instruments, J. Near Infrared Spectrosc., 24, 59, 10.1255/jnirs.1194 Hernández-Clemente, 2011, Assessing structural effects on PRI for stress detection in conifer forests, Remote Sens. Environ., 115, 2360, 10.1016/j.rse.2011.04.036 Herold, 2009, Chapter 3. VIS/NIR spectroscopy, 141 Hu, 2019, Optimization of soluble solids content prediction models in ‘Hami’ melons by means of vis-NIR spectroscopy and chemometric tools, Infrared Physics & Technology, 102, 10.1016/j.infrared.2019.102999 Huang, 2017, 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, Food Chem., 224, 1, 10.1016/j.foodchem.2016.12.037 Iglesias, 2009, The effects of reflective film on fruit color, quality, canopy light distribution, and profitability of ‘Mondial gala’ apples, HortTechnology, 19, 488, 10.21273/HORTSCI.19.3.488 Iglesias, 2012, Fruit color development, anthocyanin content, standard quality, volatile compound emissions and consumer acceptability of several ‘Fuji’ apple strains, Scientia Horticulturae, 137, 138, 10.1016/j.scienta.2012.01.029 Ignat, 2012, Non-destructive measurement of ascorbic acid content in bell peppers by VIS-NIR and SWIR spectrometry, Postharvest Biol. Tech., 74, 91, 10.1016/j.postharvbio.2012.06.010 Ignat, 2014, Forecast of apple internal quality indices at harvest and during storage by VIS-NIR spectroscopy, Food Bioprocess Technol., 7, 2951, 10.1007/s11947-014-1297-7 Igne, 2009, Improving the transfer of near infrared prediction models by orthogonal methods, Chemom. Intell. Lab. Syst., 99, 57, 10.1016/j.chemolab.2009.07.007 Ilahy, 2019, Inside and beyond color: comparative overview of functional quality of tomato and watermelon fruits, Front. Plant Sci., 10, 769, 10.3389/fpls.2019.00769 Jacobs, 2016, Estimation of the prior storage period of lamb’s lettuce based on visible/near infrared reflectance spectroscopy, Postharvest Biol. Tech., 113, 95, 10.1016/j.postharvbio.2015.11.007 Jamshidi, 2019, Ability of near-infrared spectroscopy for non-destructive detection of internal insect infestation in fruits: meta-analysis of spectral ranges and optical measurement modes, Spectrochemica Acta part A: Molecular and Biomolecular Spectroscopy, 225 Jantra, 2017, Nondestructive determination of dry matter and soluble solids content in dehydrator onions and garlic using a handheld visible and near infrared instrument, Postharvest Biol. Tech., 133, 98, 10.1016/j.postharvbio.2017.07.007 Jiménez-Cuesta, 1981, Determination of a color index for citrus fruit degreening, 2, 750 Kaur, 2017, Comparison of hand-held near infrared spectrophotometers for fruit dry matter assessment, J. Near Infrared Spectrosc., 25, 267, 10.1177/0967033517725530 Kawano, 1994, Non destructive NIR quality evaluation of fruits and vegetables in Japan, NIR News, 5, 10, 10.1255/nirn.278 Kawano, 2016, Past, present and future near infrared spectroscopy applications for fruit and vegetables, NIR News, 27, 7, 10.1255/nirn.1574 Kawano, 1992, Determination of sugar content in intact peaches by near infrared spectroscopy with fiber optics in interactance mode, J. Japan. Soc. Hort. Sci., 61, 445, 10.2503/jjshs.61.445 Khatiwadi, 2016, Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy, Postharvest Biol. Tech., 120, 103, 10.1016/j.postharvbio.2016.06.001 Knee, 1980, Methods of measuring green colour and chlorophyll content of apple fruit, Int. J. Food Sci. Technol., 15, 493, 10.1111/j.1365-2621.1980.tb00968.x Krivoshiev, 2000, 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, LWT - Food Science and Technology, 33, 344, 10.1006/fstl.2000.0669 Kuai, 2018, The biochemistry and molecular biology of chlorophyll breakdown, J. Exp. Bot., 69, 751, 10.1093/jxb/erx322 Kuckenberg, 2008, 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, Postharvest Biol. Tech., 48, 231, 10.1016/j.postharvbio.2007.10.013 Kumaravelu, 2015, A review on the applications of near-infrared spectrometer and chemometrics for the agro-food processing industries, Proceedings - 2015 IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development TIAR 2015, 8 Lammertyn, 2000, Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment, Postharvest Biol. Tech., 18, 121, 10.1016/S0925-5214(99)00071-X Li, 2016, Recent advances in nondestructive analytical techniques for determining the total soluble solids in fruits: a review, Compr. Rev. Food Sci. Food Saf., 15, 897, 10.1111/1541-4337.12217 Li, 2018, Advances in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction—a review, Plants, 7, 10.3390/plants7010003 Lichtenthaler, 1996, Detection of vegetation stress via a new high resolution fluorescence imaging system, J. Plant Physiol., 148, 599, 10.1016/S0176-1617(96)80081-2 Long, 2006, Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy, Aust. J. Agric. Res., 57, 403, 10.1071/AR05285 Lorente, 2015, Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit, J. Food Eng., 163, 17, 10.1016/j.jfoodeng.2015.04.010 Lu, 2020, Measurement of optical properties of fruits and vegetables: a review, Postharvest Biol. Technol., 159, 10.1016/j.postharvbio.2019.111003 Magwaza, 2015, Analytical methods for determination of sugars and sweetness of horticultural products—A review, Scientia Horticulturae, 184, 179, 10.1016/j.scienta.2015.01.001 Magwaza, 2015, A review of destructive and non-destructive methods for determining avocado fruit maturity, Food Bioprocess Technol., 8, 1995, 10.1007/s11947-015-1568-y McGlone, 2002, Vis/NIRS estimation at harvest of pre- and post-storage quality indices for Royal gala apple, Postharvest Biol and Tech., 25, 135, 10.1016/S0925-5214(01)00180-6 Merzlyak, 1999, Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening, Physiol. Plant., 106, 135, 10.1034/j.1399-3054.1999.106119.x Merzlyak, 2003, Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit, Postharvest Biol. Tech., 27, 88, 10.1016/S0925-5214(02)00066-2 Mukarev, 2012, Prediction of brix values of intact peaches with least squares – support vector machine regression models, J. Near Infrared Spectrosc., 20, 647, 10.1255/jnirs.1026 Nagata, 2007, A simple spectrophotometric method for the estimation of alpha-carotene, beta-carotene and lycopene concentrations in carrot [Daucus carota] acetone extracts, J. Jpn. Soc. Food Sci. Technol., 54, 351, 10.3136/nskkk.54.351 Nguyen Do Trong, 2014, Spatially resolved diffuse reflectance in the visible and near-infrared wavelength range for non-destructive quality assessment of ‘Braeburn’ apples, Postharvest Biol. Tech., 91, 39, 10.1016/j.postharvbio.2013.12.004 Nicolaï, 2007, Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review, Postharvest Biol. Tech., 46, 99, 10.1016/j.postharvbio.2007.06.024 Nicolaï, 2008, Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear, Postharvest Biol. Tech., 47, 68, 10.1016/j.postharvbio.2007.06.001 Norris, 1965, Direct spectrophotometric determination of moisture content of grain and seeds Norris, 1996, Direct spectrophotometric determination of moisture content of grain and seeds, J. Near Infrared Spectrosc., 4, 23, 10.1255/jnirs.940 Olsen, 1969, Segregation of ‘Golden delicious’ apples for quality by light transmission, J. Am. Soc. Hort. Sci., 821 Pan, 2015, Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy, Postharvest Biol. Tech., 102, 42, 10.1016/j.postharvbio.2015.02.005 Pathare, 2013, Colour measurement and analysis in fresh and processed foods: a review, Food Bioprocess Technol., 6, 36, 10.1007/s11947-012-0867-9 Peiris, 1999, 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, HortScience, 34, 114, 10.21273/HORTSCI.34.1.114 Peirs, 2003, Effect of biological variability on the robustness of NIR models for soluble solids content of apples, Postharvest Biol. Tech., 28, 269, 10.1016/S0925-5214(02)00196-5 Peñuelas, 1995, Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance, Photosynthetica, 31, 221 Pflanz, 2008, Spectrophotometric analyses of chlorophyll and single carotenoids during fruit development of tomato (solanum lycopersicum L.) By means of iterative multiple linear regression analysis, Appl. Opt., 47, 5961, 10.1364/AO.47.005961 Pierna, 2008, Soil parameter quantification by NIRS as a chemometric challenge at ‘Chimiométrie 2006’, Chemom. Intell. Lab. Syst., 91, 94, 10.1016/j.chemolab.2007.06.007 Poynton, 1997 Qin, 2008, Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique, Postharvest Biol. Tech., 49, 355, 10.1016/j.postharvbio.2008.03.010 Rady, 2018, Evaluation of carrot quality using visible-near infrared spectroscopy and multivariate analysis, Journal of Food Research, 7, 93 Roger, 2016, Orthogonal projections in the Row and the column spaces, NIR News, 27, 15, 10.1255/nirn.1640 Rouse, 1973, Monitoring vegetation systems in the Great plains with ERTS, Third ERTS Symposium, 309 Ruiz, 2008, Application of reflectance colorimeter measurements and infrared spectroscopy methods to rapid and nondestructive evaluation of carotenoids content in apricot (Prunus armeniaca L.), J. Agric. Food Chem., 56, 4916, 10.1021/jf7036032 Sadali, 2019, Differentiation of chromoplasts and other plastids in plants, Plant Cell Reports, 38, 803, 10.1007/s00299-019-02420-2 Saeys, 2019, Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: a review, Postharvest Biol. Technol., 158, 10.1016/j.postharvbio.2019.110981 Salguero-Chaparro, 2014, On-line versus off-line NIRS analysis of intact olives, Food Sci. Technol., 56, 363 Schaare, 2000, Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis), Postharvest Biol. Tech-nol., 20, 175, 10.1016/S0925-5214(00)00130-7 Seifert, 2014, Spectral shift as advanced index for fruit chlorophyll breakdown, Food Bioprocess Technol., 7, 2050, 10.1007/s11947-013-1218-1 Seifert, 2015, Optical properties of developing pip and stone fruit reveal underlying structural changes, Physiol. Plant., 153, 327, 10.1111/ppl.12232 Shenderey, 2010, NIRS detection of moldy core in apples, Food Bioprocess Technol., 3, 79, 10.1007/s11947-009-0256-1 Sims, 2003, Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features, Remote Sens. Environ., 84, 526, 10.1016/S0034-4257(02)00151-7 Srivastava, 2018, Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review, J. Food Meas. Charact., 12, 2758, 10.1007/s11694-018-9893-2 Stella, 2015, Review: recent advances in the use of non-destructive near infrared spectroscopy for intact olive fruits, J. Near Infrared Spectrosc., 23, 197, 10.1255/jnirs.1169 Subedi, 2008, Non–invasive measurement of fresh fruit firmness, Postharvest Biol. Tech., 51, 297, 10.1016/j.postharvbio.2008.03.004 Subedi, 2011, Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy, Postharvest Biol. Tech., 62, 238, 10.1016/j.postharvbio.2011.06.014 Subedi, 2020, Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: method and instrumentation optimization, Postharvest Biol. Technol., 161, 10.1016/j.postharvbio.2019.111078 Subedi, 2007, Prediction of mango eating quality at harvest, Postharvest Biol. Tech., 43, 326, 10.1016/j.postharvbio.2006.09.012 Subedi, 2012, Assessment of titratable acidity in fruit using short wave near infrared spectroscopy. Part a: establishing a detection limit based on model solutions, J. Near Infrared Spectrosc., 20, 449, 10.1255/jnirs.1010 Subedi, 2012, Assessment of titratable acidity in fruit using short wave near infrared spectroscopy. Part B: intact fruit studies, J. Near Infrared Spectrosc., 20, 459, 10.1255/jnirs.1011 Sun, 2016, Simultaneous measurement of brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy, Postharvest Biol. Tech., 116, 80, 10.1016/j.postharvbio.2016.01.009 Sun, 2020, Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content, Postharvest Biol. Technol., 162, 10.1016/j.postharvbio.2019.111117 Sun, 2020, NIRS prediction of dry matter content of single olive fruit with consideration of variable sorting for normalization treatment, Postharvest Biol. Technol., 163, 10.1016/j.postharvbio.2020.111140 Taroni, 2003, In vivo absorption and scattering spectroscopy of biological tissues, Photochem. Photobiol. Sci., 2, 124, 10.1039/b209651j Tilahun, 2018, Prediction of lycopene and ß-carotene in tomatoes by portable chromameter and VIS/NIR spectra, Postharvest Biol. Tech., 136, 50, 10.1016/j.postharvbio.2017.10.007 Timkhum, 2012, Non-destructive classification of durian maturity of ‘Monthong’ cultivar by means of visible spectroscopy of the spine, J. Food Eng., 112, 263, 10.1016/j.jfoodeng.2012.05.018 Toledo-Martín, 2016, Application of visible/near-infrared reflectance spectroscopy for predicting internal and external quality in pepper, J Sci. Food Agric., 96, 3114, 10.1002/jsfa.7488 Torricelli, 2015, Recent advances in time-resolved NIR spectroscopy for nondestructive assessment of fruit quality, Chem. Eng. Trans., 44, 43 Tripodi, 2018, Sensing technologies for precision phenotyping in vegetable crops: current status and future challenges, Agronomy, 8, 57, 10.3390/agronomy8040057 Trong, 2013, Spatially resolved spectroscopy for nondestructive quality measurements of braeburn apples cultivated in sub-fertilization condition Tsoulias, 2020, Apple shape detection based on eigen and radiometric features by means of LiDAR laser scanner, Remote Sensing, 10.3390/rs12152481 Uwadaira, 2018, An examination of the principle of non-destructive flesh firmness measurement of peach fruit by using VIS-NIR spectroscopy, Heliyon, 4, 10.1016/j.heliyon.2018.e00531 Verhoeven, 1996, Glossary of terms used in photochemistry (IUPAC recommendations 1996), Pure Appl. Chem., 68, 2223, 10.1351/pac199668122223 Vogelmann, 1993, Red edge spectra measurements from sugar maple leaves, Int. J. remote. Sensing, 14, 1563, 10.1080/01431169308953986 Walsh, 2005, Commercial adoption of technologies for fruit grading, with emphasis on NIRS Walsh, 2004, Sorting of fruit and vegetables using near infrared spectroscopy: application to soluble solids and dry matter content, J. Near Infrared Spectrosc., 12, 141, 10.1255/jnirs.419 Walsh, 2020, The uses of near infra-red spectroscopy in post-harvest decision support: a review, Postharvest Biol. Technol., 163, 10.1016/j.postharvbio.2020.111139 Wang, 2015, Fruit quality evaluation using spectroscopy technology: a review, Sensors (Switzerland), 15, 11889, 10.3390/s150511889 Wellburn, 1994, The spectral determination of chlorophyll-a and chlorophyll-b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution, J. Plant Physiol., 144, 307, 10.1016/S0176-1617(11)81192-2 Williams, 2017, Tutorial: items to be included in a report on a near infrared spectroscopy project, J. Near Infrared Spectrosc., 25, 85, 10.1177/0967033517702395 Xiao, 2018, Quality assessment and discrimination of intact white and red grapes from Vitis vinifera L. At five ripening stages by visible and near-infrared spectroscopy, Scientia Horticulturae, 233, 99, 10.1016/j.scienta.2018.01.041 Xie, 2016, Applications of near-infrared systems for quality evaluation of fruits: a review, Trans. ASABE, 59, 399, 10.13031/trans.59.10655 Xu, 2019, Factors influencing near infrared spectroscopy analysis of agro-products: a review, Frontiers of Agricultural Science and Engineering, 6, 105, 10.15302/J-FASE-2019255 Yan, 2018, Hand-held near-infrared spectrometers: State-of-the-art instrumentation and practical applications, NIR News, 29, 8, 10.1177/0960336018796391 Zarco-Tejada, 2001, Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data, IEEE Trans. Geosci. Remote Sens., 39, 1491, 10.1109/36.934080 Zarco-Tejada, 2012, Fluorescence, temperature and narrow-band indices acquired from a UAV for water stress detection using a hyperspectral imager and a thermal camera, Remote Sens. Environ., 117, 322, 10.1016/j.rse.2011.10.007 Zerbini, 2015, Optical properties, ethylene production and softening in mango fruit, Postharvest Biol. Tech., 101, 58, 10.1016/j.postharvbio.2014.11.008 Ziosi, 2008, A new index based on vis spectroscopy to characterize the progression of ripening in peach fruit, Postharvest Biol. Tech., 49, 319, 10.1016/j.postharvbio.2008.01.017 Zude, 2003, Comparison of indices and multivariate models to non-destructively predict the fruit chlorophyll by means of visible spectrometry in apples, Anal. Chim. Acta, 481, 119, 10.1016/S0003-2670(03)00070-9 Zude-Sasse, 2000, Comparative study on maturity prediction in ‘Elstar’ and ‘Jonagold’, Gartenbauwissenschaft, 65, 260 Zude-Sasse, 2002, An approach to non-destructive apple chlorophyll determination, Postharvest Biol. Tech., 25, 10.1016/S0925-5214(01)00173-9