Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis)

Schaare, Peter; Fraser, Daniel G.

doi:10.1016/s0925-5214(00)00130-7

Cited by 173 publications

(81 citation statements)

References 14 publications

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“…Compared with the full spectrum model, the siPLS model was simplified. The numbers of variables and components (830, 10) were fewer than those of the full spectrum model (3112,12). For the optimal model, RMSECV was 0.47%, and the correlation coefficient (r) was 0.92 in the calibration set.…”

Section: Spectral Preprocessingmentioning

confidence: 98%

“…Kiwifruit is one commodity where sorting based on pre-selected NIR spectral features at harvest can be used to grade fruit on the basis of DM. Recent research has established that NIR spectroscopic analysis can be used to assess kiwifruit DM and/or ripened soluble solid content using spectra below 1100 nm for greater penetration 2,5,6,11,12 . The spectra, ranging from 10 000 to 4000 cm −1 (1000-2500 nm), were composed of overtones and combinations of fundamental vibrations of -CH, -NH, and -OH groups from the mid-infrared.…”

Section: Introductionmentioning

confidence: 99%

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LüQiang¹,

Ming-Jie²,

Cai³

et al. 2010

ScienceAsia

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Synergy interval partial least square (siPLS) was proposed to select efficiently the characteristic wavelength regions of dry matter against kiwifruit near-infrared spectra for dry matter prediction. Four data sets (NIR spectra and dry matter of unripe fruit (UU), NIR spectra of unripe fruit and dry matter of ripe fruit (UR), NIR spectra and dry matter of ripe fruit (RR), and UU&UR&RR) were obtained in the experiment. They were used to develop models for predicting dry matter of unripe and/or ripe kiwifruits. The results of cross-validation showed that the change of characteristic wavelength regions was caused by chemical conversion of organic compounds included in the dry matter at different storage periods of kiwifruits. Compared with the global spectra data models, the siPLS method could simplify the models with efficiently selecting characteristic wavelength regions. The root mean square error of cross-validation and correlation coefficient (r) of the UR model were 0.47% and 0.92, respectively, in calibration set. The root mean square error of prediction and r were 0.53% and 0.90, respectively, in the prediction set. This study demonstrated that NIR spectroscopy of unripe kiwifruits could predict the dry matter.

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Section: Spectral Preprocessingmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Untitled

LüQiang¹,

Ming-Jie²,

Cai³

et al. 2010

ScienceAsia

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“…For irmness measurement, although light penetration is limited and one skin layer is still present, relection and interactance set-ups will be more desirable as the light interacts with some portion of lesh without interference from the core. Schaare and Fraser [24] compared relectance, interactance and transmission measurements for measuring soluble solid content (SSC), density and internal lesh color of kiwifruit and concluded that interactance measurements provided the most accurate results.…”

Section: Basic Conceptsmentioning

confidence: 99%

Optical Methods for Firmness Assessment of Fresh Produce: A Review

Sun¹,

Künnemeyer²,

McGlone³

2017

Postharvest Handling

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This chapter is devoted to a review of optical techniques to measure the irmness of fresh produce. Emphasis is placed on the techniques that have a potential for online high-speed grading. Near-infrared spectroscopy (NIRS) and spatially resolved relectance spectroscopy (SRRS) are discussed in detail because of their advantages for online applications. For both techniques, this chapter reviews the fundamental principles as well as the measured performances for measuring the irmness of fresh produce, particularly fruit. For both techniques, there have been studies that show correlations with penetrometer irmness as high as r = 0.8 − 0.9. However, most studies appear to involve bespoke laboratory instruments measuring single produce types under static conditions. Therefore, accurate performance comparison of the two techniques is very diicult. We suggest more studies are now required on a wider variety of produce and particularly comparative studies between the NIRS and SRRS systems on the same samples. Further instrument developments are also likely to be required for the SRRS systems, especially with an online measurement where fruit speed and orientation are likely to be issues, before the technique can be considered advantageous compared to the commonly used NIRS systems.

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“…Meanwhile, the interactance detection mode of spectroscopy, where the detector is separated from the illuminated surface by light seal in contact with fruit surface, 13 can optimize the detection of intact fruit. For these reasons, K-BA100R (Kubota, Japan) spectrophotometer (500-1010 nm, spectra interval 2 nm), equipped with¯ber-optic probe in interactance mode and charge-coupled device (CCD) line-array detector (Shimadzu, Japan), was chosen to measure the spectra of each intact melon.…”

Section: Nir Spectroscopy and Constituent (Ssc And¯rmness) Measurementsmentioning

confidence: 99%

Nondestructive determination of soluble solids and firmness in mix-cultivar melon using near-infrared CCD spectroscopy

Lü

Liu

et al. 2015

J. Innov. Opt. Health Sci.

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Nondestructive evaluation of melon quality is in great need of comprehensive study. Soluble solids content (SSC) and¯rmness are the two indicators of melon internal quality that mostly a®ect consumer acceptance. To provide guidance for fruit classi¯cation, internal quality standards was preliminarily established through sensory test, as: Melon with SSC over 12 Brix, rmness 4-5.5 kgf Á cm À2 were considered as satisfactory class sample; and SSC over 10 Brix, rmness 3.5-6.5 kgf Á cm À2 as average class sample. The near infrared (NIR) nondestructive detection program was set as spectra collected from the stylar-end, Brix expressed by the average SSC of inner and outer mesocarp, each cultivar of melon was detected with its own optimum integration time, and the second derivative algorithm was used to equalize them. Using wavelength selected by genetic algorithms (GA), a robust SSC model of mix-cultivar melon was established, the root mean standard error of cross-validation (RMSECV) was 0.99 and the ratio performance deviation (RPD) nearly reached 3.0, which almost could meet the accuracy requirement of 1.5 Brix. Firmness model of mix-cultivar melon was acceptable but inferior.

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Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis)

Cited by 173 publications

References 14 publications

Untitled

Untitled

Optical Methods for Firmness Assessment of Fresh Produce: A Review

Nondestructive determination of soluble solids and firmness in mix-cultivar melon using near-infrared CCD spectroscopy

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