Near Infrared Spectroscopy in China

Xiaoli, Chu

doi:10.1255/jnirs.1183

Cited by 5 publications

(3 citation statements)

References 10 publications

(9 reference statements)

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“…From Figure 3A , the troughs at 450 nm and 670 nm could be due to chlorophyll and other pigments in the cell wall. In comparison, the trough absorption peak at 980 nm is attributed to the tertiary and secondary frequencies of the C-H and O-H bonds in kiwifruit SSC ( Chu, 2016 ). The first and last bands of the original fluorescence spectral images were also affected by noise, so the spectral range of 400~900 nm was selected as the effective wavelength of the original fluorescence spectral images, with a total of 102 spectral bands.…”

Section: Resultsmentioning

confidence: 99%

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

Chen

Wang

et al. 2023

Front. Plant Sci.

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The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the Rp2, Rc2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the Rp2, Rc2, and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

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Section: Resultsmentioning

confidence: 99%

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

Chen

Wang

et al. 2023

Front. Plant Sci.

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“…For Vis/NIR spectral analysis, partial least square regression (PLSR) and support vector regression (SVR) were the most used as the linear and nonlinear multivariate quantitative correction methods [26]. We chose the best latent variables (LVs) to establish the PLSR model.…”

Section: Conventional Regression Modelsmentioning

confidence: 99%

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon

et al. 2023

Foods

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The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380–1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of “5 × 1” and “3 × 1” kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis.

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“…Common moisture determination methods [2] do not allow the addition of fl occulant to be adjusted in real time because of the length of the analyses. This decreases the dehydration effi ciency and entails additional consumption during production.…”

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confidence: 99%

Determination of the Ability to Measure Traces of Water in Dehydrated Residues of Waste Water by IR Diffuse Reflectance Spectra

Pratsenka

Воропай

Belkin³

2018

J Appl Spectrosc

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Rapid measurement of the moisture content of dehydrated residues is a critical problem, the solution of which will increase the effi ciency of treatment facilities and optimize the process of applying fl occulants. The ability to determine the moisture content of dehydrated residues using a meter operating on the IR refl ectance principle was confi rmed experimentally. The most suitable interference fi lters were selected based on an analysis of the obtained diffuse refl ectance spectrum of the dehydrated residue in the range 1.0-2.7 μm. Calibration curves were constructed and compared for each fi lter set. A measuring fi lter with a transmittance maximum at 1.19 μm and a reference fi lter with a maximum at 1.3 μm gave the best agreement with the laboratory measurements.

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Near Infrared Spectroscopy in China

Cited by 5 publications

References 10 publications

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon

Determination of the Ability to Measure Traces of Water in Dehydrated Residues of Waste Water by IR Diffuse Reflectance Spectra

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