The use of Vis/NIRS and chemometric analysis to predict fruit defects and postharvest behaviour of ‘Nules Clementine’ mandarin fruit

• Published in: Food chemistry Vol. 163; pp. 267 - 274
• Main Authors: Magwaza, Lembe Samukelo, Landahl, Sandra, Cronje, Paul J.R., Nieuwoudt, Hélène H., Mouazen, Abdul Mounem, Nicolaï, Bart M., Terry, Leon A., Opara, Umezuruike Linus
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.11.2014; Elsevier
• Subjects: Biological and medical sciences; Carbohydrates - analysis; Chemistry Techniques, Analytical - methods; Citrus; Citrus - chemistry; Discriminant Analysis; Food toxicology; Fruit - chemistry; Least-Squares Analysis; Medical sciences; Non-destructive; Plant Extracts - analysis; Postharvest technology; Rind breakdown; Rind physiological disorder; Spectral pre-processing; Spectroscopy, Near-Infrared - methods; Toxicology; Visible–NIR spectroscopy; Citrus; Spectral pre-processing; Rind physiological disorder; Glucose (PubChem CID: 5793); Sucrose (PubChem CID: 5988); Postharvest technology; Rind breakdown; Non-destructive; Fructose (PubChem CID: 5984); Visible–NIR spectroscopy; Fruit; Citrus fruit; Rutaceae; Postharvest; Near infrared spectrometry; Clementine; Technology; Visible-NIR spectroscopy; Dicotyledones; Angiospermae; Plant origin; Defect; Spermatophyta; Behavior; Mandarin; Chemometrics; Physiological disorder
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2014.04.085

•Fruit from inside the canopy were more susceptible to RBD disorder than outside fruit.•Vis/NIRS PLS models predicted rind fructose, glucose and sucrose with accuracy.•Vis/NIRS PCA model was able to non-destructively classify fruit based on tree canopy.•Models based on pre-storage spectra gave better prediction of RBD than post-storage. The use of chemometrics to analyse Vis/NIRS signal collected from intact ‘Nules Clementine’ mandarin fruit at harvest, to predict the rind physico-chemical profile after eight weeks postharvest was explored. Vis/NIRS signals of 150 fruit were obtained immediately after harvest. Reference data on the rind were obtained after eight-week storage, including colour index (CI), rind dry matter (DM), and concentration of sugars. Partial least squares (PLS) regression was applied to develop models. Principal component analysis (PCA) followed by PLS-discriminant analysis (PLS-DA) were used to classify fruit according to canopy position. Optimal PLS model performances for DM, sucrose, glucose and fructose were obtained using multiple scatter correction pre-processing, showing respective residual predictive deviation (RPD) of 3.39, 1.75, 2.19 and 3.08. Clusters of sample distribution in the PCA and PLS-DA models based on canopy position were obtained. The results demonstrated the potential applications of Vis/NIRS to predict postharvest behaviour of mandarin fruit.