Non-invasive measurements of ‘Yunhe’ pears by vis-NIRS technology coupled with deviation fusion modeling approach

Yuan, Lei‐ming; Mao, Fei; Chen, Xiaojing; Li, Limin; Huang, Guangzao

doi:10.1016/j.postharvbio.2019.111067

Cited by 29 publications

(12 citation statements)

References 33 publications

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“…In July 2020, a total of 195 ‘Snow’ pears were purchased from a reliable local fruit market in Chongqing, China. In order to ensure the accuracy of the experiment, pears were washed, numbered, and then stored in an ice box (4℃, 75%RH) for 24 h before the experiment was carried out (Yuan et al, 2020). Figure1 showed that three measuring points (P1, P2, and P3) around the equator of pear were set as a 120° angular distance.…”

Section: Methodsmentioning

confidence: 99%

Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics

Liu

et al. 2021

Food and Energy Security

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Lignin plays an important role in the formation of stone cells in pears. However, the accumulation of lignin had adverse impact on the flavor and quality of the fruit. A rapid and accurate method for measuring the lignin content of pears is therefore required. An improved variables selection method called ‘the bootstrapping soft shrinkage combined with frequency and regression coefficient of variables (FRCBOSS)’ was therefore developed based on ‘the bootstrapping soft shrinkage (BOSS)’technique, to identify the characteristic wavelengths of near‐infrared (NIR) spectra for non‐destructive and rapid analysis of lignin. Sub‐models were generated by weighted bootstrap sampling (WBS) in the FRCBOSS method. For the BOSS method, the new weights of variables were determined only as the absolute values of regression coefficients of variables in each iteration. In contrast, the FRCBOSS method also considers the frequency of variables in variable space. Moreover, the FRCBOSS algorithm overcomes the disadvantage of BOSS in selecting variables, which could incorporate useful wavelengths that would otherwise be removed by the BOSS method. In addition, a range of different pre‐treatment methods were used for comparison in the detection of lignin in the Snow pears. These include Savitzky–Golay Smoothing (SG), Normalization (NORM), Standard Normal Variate (SNV), and 1st Derivative (D1), as well as a combination of these methods and the different variables selection method (SiPLS, SiPLS‐SPA, SiPLS‐CARS, SiPLS‐BOSS, and SiPLS‐FRCBOSS). The number of variables selected by FRCBOSS was a little larger than that selected by BOSS. The partial least square regression (PLSR) model based on the 19 variables selected by SiPLS‐FRCBOSS method had the best prediction ability, with a Rp value of prediction of 0.880 and a RMSEP value of 1.004%. We conclude that NIR diffuse reflectance spectroscopy technology combined with FRC‐BOSS is an accurate and useful tool for the non‐destructive and rapid determination of pear lignin contents.

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Section: Methodsmentioning

confidence: 99%

Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics

Liu

et al. 2021

Food and Energy Security

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“…We purchased a total of 195 snow pears, a representative China pear, from a local fruit market for this research. To ensure the accuracy of the experiment, pears are washed, numbered, and then stored in an ice box (4 • C, 75% relative humidity (RH)) for 24 h to avoid moisture loss before the experiment was carried out [37]. Figure 1 shows that three measuring points (P1, P2, and P3) around the equator of the pear were set as a 120 • angular distance.…”

Section: Sample Collectionmentioning

confidence: 99%

Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing

2021

Foods

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The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears.

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“…Mean centering (MC) provides that the dataset has zero mean, which is done by subtracting the mean value of the dataset from the dataset. MC was used in López-Maestresalas et al and Yuan et al [26,45]. De-trending (DT) is a method used to correct baseline changes and curvilinear spectrum patterns.…”

Section: Savitzky-golay Filtermentioning

confidence: 99%

“…With the MLR model, better results were obtained for five parameters. Yuan et al applied PLS and MLR regression models and their deviation fusion to analyze "Yunhe" pears attribute with NIRS, and as a result, they obtained a successful result with Rp value of 0.9026 [45]. Berhow et al applied SG pre-processing method to the spectral data created to determine the isoflavone and saponin content of ground soybeans and modeled them with MLR.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

A Review of Data Analysis Techniques Used in Near-Infrared Spectroscopy

Cataltas¹,

Tutuncu²

2021

European Journal of Science and Technology

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Although the analysis of the structure of objects and the components that makeup them has been done for decades, it is one of today's research topics to do this analysis quickly and without damaging the sample. Near-infrared spectroscopy is used in many areas due to its non-contact measurement, fast analysis, and high accuracy features. Near-infrared spectroscopy is used in the classification or quality analysis of products, especially in the agriculture and food sector, due to the chemical bonds interacting in this region. The most critical part of achieving a successful result in near-infrared spectroscopy is pre-processing and analyzing the spectral data using the correct method. In this review, we perform a survey of recent studies that use near-infrared spectroscopy in food production and agriculture. Since there are many studies in this field in the literature, the survey is limited to cover works in the last five years. The review's main question is the pre-processing and data analysis methods used in these studies and the main features of these methods. Among the examined studies, the most frequently used pre-processing method was standard normal variate, and the most frequently used analysis method was partial least squares regression. In addition, the software tools and the spectrum range were also examined within the scope of the study.

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Non-invasive measurements of ‘Yunhe’ pears by vis-NIRS technology coupled with deviation fusion modeling approach

Cited by 29 publications

References 33 publications

Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics

Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics

Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing

A Review of Data Analysis Techniques Used in Near-Infrared Spectroscopy

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