Models to improve the non‐destructive analysis of persimmon fruit properties by <scp>VIS</scp>/<scp>NIR</scp> spectrometry

Altieri, Giuseppe; Genovese, Francesco; Tauriello, Antonella; Renzo, Giovanni Carlo Di

doi:10.1002/jsfa.8416

Cited by 19 publications

(21 citation statements)

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“…The calibration model accuracy was described by the coefficient of determination for the calibration data set (R 2 c ) and validation data set (R 2 p ), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). In addition, the ratio of prediction to deviation value (RPD) was calculated [9,22] by dividing the standard deviation (SD) to the RMSECV or the RMSEP. RPD results below 1.5 indicated that the calibration was not useful.…”

Section: Chemometric Analysismentioning

confidence: 99%

Estimation of Ascorbic Acid in Intact Acerola (Malpighia emarginata DC) Fruit by NIRS and Chemometric Analysis

et al. 2019

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Acerola fruit is one of the richest natural sources of ascorbic acid ever known. As a consequence, acerola fruit and its products are demanded worldwide for the production of health supplements and the development of functional products. However, the analytical determination of ascorbic acid is time-consuming and costly. In this study, we show a non-destructive, reliable, and fast method to measure the ascorbic acid content in intact acerola, using near-infrared spectroscopy (NIRS) associated with multivariate calibration methods. Models using variable selection by means of interval partial least squares (iPLS) and a genetic algorithm (GA) were tested. The best model for ascorbic acid content, based on the prediction performance, was the GA-PLS method with second derivative spectral pretreatment, with a root mean square error of cross-validation equal to 22.9 mg/100 g, root mean square error of prediction equal to 46.3 mg/100 g, ratio of prediction to deviation equal to 8.0, determination coefficient for calibration equal to 0.98 and determination coefficient for prediction equal to 0.96. The current methodology, using NIR spectroscopy and chemometrics, is a promising and rapid tool to determine the ascorbic acid content of intact acerola fruit.

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Section: Chemometric Analysismentioning

confidence: 99%

Estimation of Ascorbic Acid in Intact Acerola (Malpighia emarginata DC) Fruit by NIRS and Chemometric Analysis

et al. 2019

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“…Statistical multilinear correlation, built using spectra in the visible range (VIS) (380–740 nm) and / or in the near‐infrared range (NIR) (700–2500 nm), allows the quick prediction of some important properties of agricultural foodstuffs, as has been demonstrated for milk, cereals, vegetables and fruits, during fruit processing operations, and also when monitoring fungicide content in water during the dipping treatment of citrus fruit . The results of the NIR analysis confirm the possibility of measure oil and water content in the olive paste and pomace …”

Section: Introductionmentioning

confidence: 60%

“…The forecasting capability must be statistically quantified using techniques such as cross‐validation (CV), involving the random partitioning of samples into complementary subsets such as k‐fold CV, or leave‐one‐out CV. These methods allow the prediction capability of the model to be estimated, measured by the widespread ratio of standard deviation of calibration data to standard error of prediction data (RPD), as described extensively in the literature . An RPD ≥5.0 indicates that the model can be used in quality control.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, as many factors influence the spectral data, producing bias and non‐linearity, some data pre‐treatments are needed. The most common data pre‐treatments are multiplicative scatter correction, standard normal variate correction, de‐trending or baseline correction, data standardization, and the use of first and second derivatives …”

Section: Introductionmentioning

confidence: 99%

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Models for the rapid assessment of water and oil content in olive pomace by near‐infrared spectrometry

et al. 2020

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BACKGROUND The measurement of the water and oil content in olive pomace is crucial for controlling the olive‐oil extraction process. The use of near‐infrared (NIR) spectra could allow the measurement of the oil and water content in olive pomace. RESULTS Partial least squares for pomace oil content on a dry basis reached an error of 2.5% (±0.5). Principal component regression for pomace oil content on a wet basis reached an error of 3.7% (±0.5). Both were suitable for quantitative analysis. Principal component regression for pomace water content reached an error of 6.0% (±2.3), suitable for process control. The relationship between ‘ratio of standard deviation of calibration data to standard error of prediction data’ and ‘range of confident prediction error percentage’ was investigated, it results of hyperbolic type, the constant of the hyperbolic equation depends on the product under analysis: for the olive pomace this constant is equal to 45.60 (±1.78). CONCLUSION Near‐infrared analysis confirmed the possibility of determining the oil and water content in the olive pomace, which is important in the olive oil extraction process control. A new algorithm was used, together with standard statistical algorithms, to identify and remove the less useful wavelengths from the model, improving the overall prediction performance. A new parameter (the ‘range of confident prediction error percentage’) has been proposed for estimating the model's prediction error in an objective way. © 2020 Society of Chemical Industry

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“…So far, NIR technology has been successfully applied to detect the internal quality of fruits with thin peel, such as pears, peaches, apples, kiwifruits and persimmons, and so on (Altieri, Genovese, Tauriello, & Direnzo, 2017;Bobelyn et al, 2010;Clark et al, 2004;Fan, Zhang, Li, Huang, & Wang, 2016;Nascimento et al, 2016;Nicolaï, Verlinden, Desmet, & Torricelli, 2008). However, compared with this type of fruit, it may be more difficult to assess the internal quality of fruits with thick peel such as melons and watermelons by NIR spectroscopy (Ito et al, 2002), because effective collection of the spectral information is more difficult for fruits with thick peel.…”

Section: Introductionmentioning

confidence: 99%

Near‐infrared light penetration depth analysis inside melon with thick peel by a novel strategy of slicing combining with least square fitting method

Zhang

2018

J Food Process Engineering

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The light penetration depth in melon was explored by near‐infrared (NIR) diffuse reflectance spectroscopy (500–1,100 nm) combining a novel strategy of slicing and least square fitting. The depths of peak wavelength and effective waveband (720–880 nm) based on three source‐detector distances (d = 10, 20, and 30 mm) were calculated and compared by least square fitting. The results indicated that the obtained depth were similar both slicing method and least square fitting. The depth values of melon fruits were 12.3, 14.9, and 19.0 mm for peak wavelength, and the depth value ranges were 11.7–12.4, 14.2–15.8, and 16.8–21.0 mm for the effective waveband of 720–880 nm when d was 10, 20, and 30 mm, respectively. The overall results demonstrated that least square fitting method could be accurately applied to calculate the light penetration depth inside melon and the depth value increased with increasement of d. And 20 mm source‐detector distance could be the optimal selection. This study can provide the valuable reference for development of spectral instrument used to effectively detect the internal quality of fruits, especially for fruits with thick peel. Practical application The light penetration depth in fruit tissues was very important for obtaining the more effective spectral information used to accurately and robustly assess the internal quality of fruits. Currently, there was no effective and rapid technology to measure the light penetration depth of fruits, especially fruits with thick peel. Hence, in this research, we provided a novel method of slicing and least square fitting to calculate the light penetration depth in melon based on different source‐detector distance. The result indicated that the proposed method could be applied to calculate the penetration depth in melon and provided a valuable reference to develop the spectral equipment.

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Models to improve the non‐destructive analysis of persimmon fruit properties by VIS/NIR spectrometry

Cited by 19 publications

References 50 publications

Estimation of Ascorbic Acid in Intact Acerola (Malpighia emarginata DC) Fruit by NIRS and Chemometric Analysis

Estimation of Ascorbic Acid in Intact Acerola (Malpighia emarginata DC) Fruit by NIRS and Chemometric Analysis

Models for the rapid assessment of water and oil content in olive pomace by near‐infrared spectrometry

Near‐infrared light penetration depth analysis inside melon with thick peel by a novel strategy of slicing combining with least square fitting method

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