Multispectral scattering imaging and NIR interactance for apple firmness predictions

Sun, Jason; Künnemeyer, Rainer; McGlone, Andrew; Rowe, Philip

doi:10.1016/j.postharvbio.2016.04.019

Cited by 39 publications

(22 citation statements)

References 33 publications

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“…In implementing spectral scattering imaging, a broadband light source is used to illuminate the sample and backscattered images are acquired by a camera coupled with a wavelength selection device. In recent years, researchers have explored laser-based imaging systems for fruit quality assessment [44][45][46][47], which use a laser module as a light source that consists of several lasers operating at different wavelengths to acquire multispectral scatting images.…”

Section: Spectral Scattering Imagingmentioning

confidence: 99%

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

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Section: Spectral Scattering Imagingmentioning

confidence: 99%

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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“…Typically, images are irst processed to reduce noise and then converted into one-dimensional proile [29]. Figure 5 illustrates the process used by Sun et al [30] for measuring apple irmness. The scatering image was irst processed to ind the center of the illuminated area ( Figure 5(a)).…”

Section: Spatially Resolved Relectance Spectroscopy (Srrs)mentioning

confidence: 99%

“…They concluded Eq. (1) was the best performing equation, which was also used in other studies for irmness applications [7,29,30]:…”

Section: Parameters Extractionmentioning

confidence: 99%

“…One of the drawbacks of LLBI systems is the limited operating wavelength. One to four lasers are typically used [28,30,34]. Van Beers et al [35] Peach "Red Haven"…”

Section: Hardwarementioning

confidence: 99%

“…However, it requires a good contact/ focus between the probes and the sample, which will not be suitable for online operations. A CCD camera is more commonly used as it is noncontact, which has been a dominant format in all three types of systems ( Table 2), except that Sun et al [30] used a CMOS camera. CCD and CMOS cameras allow only single wavelength operation, but an imaging spectrograph has been used in HLBI systems to provide spectral and spatial information on a single image (Figure 6).…”

Section: Postharvest Handlingmentioning

confidence: 99%

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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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Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths

Zhu

2019

Food Science & Nutrition

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Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x‐loading weights (x‐LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of “Fuji” and “Qinguan” apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for “Fuji” and “Qinguan” apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the “Fuji” apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x‐LW for the “Qinguan” apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for “Fuji” and “Qinguan” apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both “Fuji” and “Qinguan” apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection.

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Multispectral scattering imaging and NIR interactance for apple firmness predictions

Cited by 39 publications

References 33 publications

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Optical Methods for Firmness Assessment of Fresh Produce: A Review

Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths

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