Evaluation of NIRS as non-destructive test to evaluate quality traits of purple passion fruit

Maniwara, Phonkrit; Nakano, K.; Ohashi, Shintaroh; Boonyakiat, Danai; Seehanam, Pimjai; Theanjumpol, Parichat; Poonlarp, Pichaya

doi:10.1016/j.scienta.2019.108712

Cited by 26 publications

(12 citation statements)

References 27 publications

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“…The statistical parameters used in this study included the determination coefficient of calibration (R 2 c ), the determination coefficient of prediction (R 2 p ), the root mean square error of calibration (RMSEC), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD). The closer R 2 p is to 1, the smaller and closer the values of RMSEC and RMSEP are, which indicates a high accuracy of the F I G U R E 1 Structure of the optical fiber spectroscopy system established models (Li et al, 2015;Maniwara et al, 2019). We also employed the RPD, the ratio of the standard deviation (SD) to the root mean square error of prediction.…”

Section: Spectral Data Analysismentioning

confidence: 99%

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Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

Liu

Qiao

Hou

et al. 2021

J Food Process Engineering

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Moisture content (MC) directly affects the storage of apples. This study explored the prediction of MC and the shelf life of apples stored at room temperature. Using wavelengths selected by the competitive adaptive reweighted sampling algorithm and chemical kinetic reactions, a quantitative prediction model based on optical fiber spectroscopy and a kinetic model were established to determine the MC of apples stored at room temperature. The shelf life of apples was predicted based on the developed quantitative prediction and kinetic models. The determination coefficients of the calibration and prediction of the quantitative prediction model were determined as .9810 and .9686, with root mean square errors of .0020 and .0022, respectively, and a residual predictive deviation of 6.55. In addition, the correlation coefficient of the kinetic model was derived as .9957, with a root mean square error of .0011. Results demonstrate that when the MC of the fresh apples exceeds 85% and the initial MC of apples stored at room temperature equals 87%, the freshness preservation time of the apples should not exceed 28 days. The proposed quantitative and kinetic models are able to rapidly and accurately monitor the MC of apples, and the real-time prediction of apple shelf life can be realized according to the difference in the spectral information pre-and post-storage at room temperature. Practical ApplicationThis study provides a method for the determination of moisture content (MC) in apples and the prediction of shelf life based on kinetic models. According to the proposed method, enterprises can not only quickly and accurately detect the MC of apples to determine whether they are fresh, but can also easily predict the shelf life of apples to determine whether they are safe. | INTRODUCTIONApple is one of the most popular and important fruits worldwide. In particular, apple is favored by consumers because of its antioxidant, anti-aging, and immunity-enhancing functions (Briones-

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Section: Spectral Data Analysismentioning

confidence: 99%

“…We also employed the RPD, the ratio of the standard deviation (SD) to the root mean square error of prediction. The higher the RPD value, the better the prediction effect of the developed models, and vice versa (Li et al, 2015;Maniwara et al, 2019).…”

Section: Spectral Data Analysismentioning

confidence: 99%

Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

Liu

Qiao

Hou

et al. 2021

J Food Process Engineering

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show abstract

“…The performance of two algorithms combined with the mentioned pre-processing techniques was compared by use of the statistical parameters, such as the coefficient of determination (R 2 ) between the predicted values and actual values of each observation, the root means square error of calibration and prediction (RMSEC and RMSEP) and the ratios of performance to deviation, known as the residual predictive deviation values (RPD). 35 The calculations of RMSEP (the definition is analogous for RMSEC), Bias (predictive fault), and RPD are defined in the following equations. In PLS analysis, the number of variables was optimized into factors, including too many factors in the PLS model that may lead to overfitting, whereas too few factors may result in underfitting.…”

Section: / 14mentioning

confidence: 99%

Quantification of potassium concentration with Vis–SWNIR spectroscopy in fresh lettuce

Xiong

Ohashi

Nakano

et al. 2020

J. Innov. Opt. Health Sci.

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“…The results of a variety of spectroscopic and chemometric techniques have proven that NIR spectroscopy alone is quite effective for determining the physical and chemical properties of several fruits [ 13 , 14 , 15 ]. Other applied research in the field of spectroscopy that can be mentioned separately include: apple [ 16 ], sesame [ 17 ], pear [ 18 ], passion fruit [ 19 ], jujube [ 20 ], pomegranate [ 21 ], mango [ 2 ], grapes [ 22 ], and tangerine [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…The ability of spectroscopy to identify the geographical origin of oilseeds and edible oils was studied. Maniwara et al [ 19 ] estimated soluble solid contents (SSC), titratable acidity (TA), and the pulp content (PC) of purple berry fruit using NIR spectroscopy. They developed prediction models based on partial least squares (PLS) at the range of NIR spectrum.…”

Section: Introductionmentioning

confidence: 99%

Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods

Sharabiani

Sabzi

Szymanek

et al. 2021

Foods

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Fruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive and time-consuming methods that have no application for online operations. For that, various researchers have conducted various studies on non-destructive methods, which are currently in the research and development stage. Thus, the present paper focusses on a non-destructive method based on spectral data in the 200–1100-nm region for estimation of total soluble solids and BrimA in Gala apples. The work steps included: (1) collecting different samples of Gala apples at different stages of maturity; (2) extracting spectral data of samples and pre-preprocessing them; (3) measuring the chemical properties of TSS and BrimA; (4) selecting optimal (effective) wavelengths using artificial neural network-simulated annealing algorithm (ANN-SA); and (5) estimating chemical properties based on partial least squares regression (PLSR) and hybrid artificial neural network known as the imperialist competitive algorithm (ANN-ICA). It should be noted that, in order to investigate the validity of the methods, the estimation algorithm was repeated 500 times. In the end, the results displayed that, in the best training, the ANN-ICA predicted the TSS and BrimA with correlation coefficients of 0.963 and 0.965 and root mean squared error of 0.167% and 0.596%, respectively.

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Evaluation of NIRS as non-destructive test to evaluate quality traits of purple passion fruit

Cited by 26 publications

References 27 publications

Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

Quantification of potassium concentration with Vis–SWNIR spectroscopy in fresh lettuce

Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods

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