Vis-NIR高光谱技术测番茄SSC：多参数补偿模型应用

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"这篇研究论文探讨了通过应用可见光到近红外（Vis-NIR）高光谱透射成像技术和多参数补偿模型来定量评估加工番茄（Lycopersicon esculentum Mill.）中的可溶性固形物含量（SSC）。" 文章详细介绍了在果蔬加工领域中，尤其是对番茄制品，SSC是一个极其关键的质量评价指标，它直接影响到加工番茄产品的品质和成本，比如番茄酱。传统的SSC测量方法通常依赖于实验室的化学分析，这些方法费时且可能破坏样品。因此，寻找快速、无损且准确的SSC测量技术至关重要。该研究提出了一种新的分析方法，利用Vis-NIR高光谱透射成像技术，该技术可以获取物体内部的光谱信息，通过对光谱数据的处理和分析，可以揭示番茄中SSC的分布情况。高光谱成像技术结合了空间和光谱信息，使得对样品的非接触、无损检测成为可能。此外，为了提高测量的准确性，研究还引入了多参数补偿模型。这种模型能够校正环境因素（如光照变化、温度影响等）和样本自身特性（如成熟度、颜色差异等）对SSC测量结果的影响。通过多参数补偿，可以更精确地估计出番茄的SSC含量，从而改善预测性能。实验结果显示，结合Vis-NIR高光谱透射成像和多参数补偿模型的方法在SSC的定量评估中表现出良好的潜力，为加工番茄的在线质量控制提供了新的可能性。这种方法不仅适用于生产线上实时监控产品质量，还有助于优化加工过程，降低生产成本，提高产品一致性。研究进一步指出，尽管取得了积极的结果，但还需要更多的实验数据来验证和优化模型的普适性。未来的研究可能包括不同品种、不同成熟阶段的番茄，以及在各种环境条件下测试模型的稳定性。国家自然科学基金资助了这项研究，编号为61565016，表明了对这一领域的重视和支持。研究者期待这种新技术能被广泛应用于食品工业，推动番茄加工行业的技术创新和质量提升。

of white radishes for detecting hollowness defects, respectively. The

detection results indicated that transmittance and semi-transmittance

imaging modes coupled with the back propagation artificial neural

network model could get more satisfactory identification accuracies

for classifying between normal and hollowness. All these studies

showed that hyperspectral imaging with transmittance mode was

potential for internal quality assessment of horticultural products due

to the fact that the deeper information from the detected object can

be obtained. However, as far as we know, few works have focused on

measuring the SSC of horticultural products by using hyperspectral

transmittance imaging technique. Therefore, it might be the first time

that this technique was utilized to quantitatively determinate the SSC

in processing tomatoes. In addition, in terms of hyperspectral imaging

for SSC detection of horticultural products, it should be pointed out

that most of researches used samples with similar size for avoiding

size influence on the detection accuracy of models. This is clearly less

attractive for on-line real grading application. In this work, we used

different size of samples for more closely aligned with the actual

applications. However, transmittance spectra of tomatoes with differ-

ent sizes have the different optical path (Clark, McGlone, & Jordan,

2003; Guthrie, Walsh, Reid, & Liebenberg, 2005; Krivoshiev,

Chalucova, & Moukarev, 2000; Magwaza, Opara, Cronje, Saeys, &

Nicolaï, 2012). Moreover, in the preliminary experiments, we also

found that processing tomatoes with the same sizes may have differ-

ent weights, showing the densities of different tomatoes have a dif-

ference. Density difference also affects the optical path and further

affects the prediction accuracy of models. Therefore, this study also

tried to develop the multi-parameter compensation model by combin-

ing with spectrum, size and weight information of processing toma-

toes. The main goals of this article were as follows:

1. To set up a hyperspectral imaging system with the transmittance

mode providing spectral information of 400–1,000 nm and evalu-

ate its capacity of using hyperspectral transmittance imaging to

quantitatively analyze the SSC in processing tomatoes;

2. To identify those important wavelengths that represent the most

useful information for the measurement of SSC of processing

tomatoes by using competitive adaptive reweighted sampling

(CARS) algorithm for development of low-priced and on-line multi-

spectral imaging detection equipment;

3. To establish the multi-parameter compensation linear PLSs models

and nonlinear least squares-support vector machine (LS-SVM)

models and further compare performance of these models in the

SSC evaluation of processing tomatoes.

2 | MATERIALS AND METHODS

2.1 | Processing tomatoes

In total, 200 processing tomatoes with all red peel were collected

from local plantation in Shihezi City (Xinjiang province, China) during

the harvest season in August 2017. Because defects can affect the

spectral characteristics, the used samples have no any surface defects

or bruises. However, two samples were found with the internal rot-

tenness when all samples were destructively measured for obtaining

the real SSC. Thus, 198 processing tomatoes were used in this study.

Different from existing works, samples with similar sizes were usually

used to establish the models for reducing the influence of sizes on

SSC detection accuracy. In this study, all processing tomatoes with

the transverse equatorial diameter range of 45–76 mm were directly

used to acquire the hyperspectral images according to real application

of on-line grading. Before images data acquisition, samples were first

marked and then placed in the lab with 60% relative humidity and



C environment temperature for more than 2 days (Cozzolino

et al., 2007).

2.2 | Three-dimensional (3D) data acquisition system

A laboratory Vis-NIR transmittance hyperspectral imaging system was

constructed and utilized to acquire spectral images of processing

tomatoes. The constructed imaging system and the acquired 3D data

were shown in the left and right of Figure 1, respectively. The sys-

tem consisted of an imaging spectrograph (ImSpector V10E-QE,

Spectral Imaging Ltd., Oulu, Finland), a high sensitive CCD camera

(C8484-05G01, Hamamatsu Photonics, Japan) with a standard C-

mount zoom lens (V23-f/2.4, Specim Ltd., Finland), an illumination

unit with 150 W halogen lamp (SCHOTT DCRIII, SCHOTT North

America, Inc., Elmsford, NY, USA), an electronically-controlled

motorized horizontal stage (PSA-200-11-X,Zolix Inc. Beijing, China)

and a computer (Lenovo, Inter(R)Core(TM)i3-3240 CPU@3.4 GHz,

RAM 4.00 GB). The image acquisition software (SpecViev Isuzu

Optics Corp., Taiwan) and movement stage control softwar e (Isuzu

Optics Corp., Taiwan) were installed on the computer. In the terms

of transmittance mode, the halogen lamp was fixed directly below

the tested sample. Sample horizontal movement speed and camera

exposure time were set at 15 mm/s and 5 ms, respectively, to avoid

image distortion. I n order to reduce interference from ambient light,

a black rubber seal was set in the optical round hole between halo-

gen lamp and the tested sample. The image acquisition system can

obtain the images covering the spectral range of 400–1,000 nm with

the wavelength interval of ~1.27 nm. Thus, a total of 473 single-

wavelength images were produced in a r aw hyperspectral image.

Hype rspectral image of each sample was recorded as a 3D data

block including image information in the direction of axis x-y and

spectral information in the direction of axis λ, a s shown in the left

side of Figure 1.

2.3 | Original hyperspectral image correction

Halogen lamp produces a spatial intensity variation in the field of view

of camera and the dark current also exists in the CCD camera, which

could result in the obvious noises at both ends of wavelengths due to

relative low illumination. Therefore, the original spectral images (R

)

were necessary to perform the flat field calibration on the basis of

dark reference (R

) and white reference (R

). The image without any

light was collected as dark reference by covering the camera lens and

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