Relationship between sensory and NIR spectroscopy in consumer preference of table grape (cv Italia) |
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| Institution: | 1. Department of Agricultural and Food Sciences, University of Bologna, Piazza Goidanich 60, Cesena (FC) 47521, Italy;2. Department of Agricultural Sciences, University of Bologna, Viale G. Fanin 44, 40127 Bologna, Italy;3. COOP Italia, Via del Lavoro 1, Casalecchio di Reno (BO) 40033, Italy;1. Department of Viticulture and Oenology, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa;2. Departamento de Tecnologia de Alimentos, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain;3. Institute for Wine Biotechnology, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa;1. School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China;2. Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China;3. The Yunnan Tea Chamber of Commerce, Panlong District, Kunming, Yunnan 650051, China;4. The Department of Tea, Guizhou Vocational College of Agriculture, 3 Huangshi Rd, Qingzhen, Guizhou 551400, China;5. The Guizhou Gui Tea (Group) Co. Ltd, Huaxi District, Guiyang, Guizhou 550001, China;6. Innovation Laboratory, the Third Experiment Middle School in Guiyang, Guiyang, Guizhou 550001, China;1. SMART Research Institute, PT. SMART Tbk, Riau, Indonesia;2. Institute of Statistics, College of Arts and Sciences, University of the Philippines Los Baños, Laguna, Philippines;3. Institute of Mathematical Sciences and Physics, College of Arts and Sciences, University of the Philippines Los Baños, Laguna, Philippines;4. Institute of Renewable Natural Resources, College of Forestry and Natural Resources, University of the Philippines Los Baños, Laguna, Philippines;1. Departamento de Producción Agropecuaria, Facultad de Ciencias Agropecuarias y Forestales, Universidad de La Frontera, Avenida Francisco Salazar 01145, P.O. Box 54-D, Temuco, Chile;2. Center of Plant-Soil Interaction and Natural Resources Biotechnology, Scientific and Technological Bioresources Nucleus (BIOREN-UFRO), Universidad de La Frontera, Avenida Francisco Salazar 01145, P.O. Box 54-D, Temuco, Chile;3. FA.MO.SA S.r.l. (FArm MOnitoring Systems for Agriculture), Via Selice 84/a, 40026 Imola, Italy;4. Facultad de Ciencias Agropecuarias y Forestales, Universidad de La Frontera, Avenida Francisco Salazar 01145, P.O. Box 54-D, Temuco, Chile;5. Department of Agricultural Sciences, School of Agriculture and Veterinary Medicine, Alma Mater Studiorum, University of Bologna, Viale G. Fanin 44, 40127 Bologna, Italy;1. CITAB-Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal;2. INOV – INESC Inovação, Rua Alves Redol, 9, 1000-029 Lisboa, Portugal;3. Departamento de Engenharias, Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal;4. BioISI – Biosystems & Integrative Sciences Institute, University of Lisboa, Faculty of Sciences, Campo Grande, Lisboa, Portugal;5. Departamento de Genética e Biotecnologia, Escola das Ciências da Vida e Ambiente, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal |
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| Abstract: | A combination of near infrared spectroscopy (NIR) instrumental measurements and sensory analysis was investigated to predict solids soluble content (SSC, assessed as Brix) and to classify preference in table grape cv Italia. SSC was monitored in each berry of whole bunches in order to evaluate intra-bunch distribution and variability. NIR spectra were recorded in the spectral region 12,000–4000 cm?1 (833–2500 nm) using a set of 682 berries. The Partial Least Square (PLS) model based on cross-validation provided acceptable value for the main statistical parameters (coefficient of determination of cross-validation, r2: 0.85; standard error of cross-validation, SECV: 1.08; residual predictive deviation, RPD: 2.6) and was confirmed by external validation performed with 115 independent berries (coefficient of determination of prediction, rp2: 0.82; standard error of prediction, SEP: 0.83). For consumer testing, the selected PLS model was used to predict the Brix value in 400 berries and Discriminant Analysis (DA) was then carried out to classify berries in terms of preference by relating NIR data to consumer judgment. The three defined preference clusters of berries were fully classified obtaining 100% membership. In cross-validation the value decreased especially for class 1 (78.5%) and 3 (75%) whereas class 2 obtained comparable values (98.7%). According to our results, NIR technology appears to be a promising technique for predicting SSC and obtaining information with regard to consumer preference in ‘Italia’ table grape for application of efficient and low cost on-line instruments in the fruit industry. |
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| Keywords: | Table grape Near Infrared spectroscopy Sensory Consumers Classification |
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