# Intelligent Evaluation of Stone Cell Content of Korla Fragrant Pears by Vis/NIR Reflection Spectroscopy

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Korla Fragrant Pears and Pretreatment

#### 2.2. Vis/NIR Spectroscopy System and Diffuse Reflectance Spectra Acquisition

#### 2.3. Measurement of SCC

_{i}refers to the SCC; m

_{itotal}refers to the total weight of filter paper and stone cells; m

_{ifilter}refers to the weight of filter paper; and m

_{i}refers to the weight of the selected pulp.

#### 2.4. Spectral Preprocessing and Sample Set Division

#### 2.5. Algorithms of Selecting Characteristic Wavelengths

#### 2.5.1. SPA

_{v}(i) refers to the measured value of SCC of sample i in Vs; ${\widehat{\mathrm{Ref}}}_{\mathrm{v}}\left(\mathrm{i}\right)$ refers to the predicted SCC value calculated by selected spectral data and B.

_{RMSEV}, was calculated by the inverse function of the sum distribution function for the F distribution, as shown by Equation (4), for which the significance value α was 0.25 and the degrees of freedom were the same. The wavelengths whose RMSEVs were less than t

_{RMSEV}were chosen as the final characteristic ones.

#### 2.5.2. UVE Combined with Monte Carlo Sampling (MCUVE) and PLSR

#### 2.6. Modeling Algorithm

## 3. Results

#### 3.1. Statistics of SCC Measured Values

#### 3.2. Spectral Characteristics and Different Preprocessing Methods

_{(7, 5)}had lower Rs and higher RMSEs, in Cs and Vs. The robustness of the PLSR model based on SNV was better than that of MSC according to the different values of Rs between Cs and Vs. The addition of S-G

_{(7, 5)}did not improve the ability of evaluation because the combination of two-point smoothing and S-G

_{(7, 5)}eliminated some effective spectral information. The model established on the basis of the SNV preprocessing algorithm achieved the best results, with R and RMSE of 0.9189 and 0.0277% in the Cs, and 0.8935 and 0.0315% in the Vs. Spectral curves based on SNV are shown in Figure 2b.

#### 3.3. Characteristic Wavelengths

#### 3.4. SCC Evaluation Based on PSO-SVR

## 4. Discussion

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Vis/NIR spectra acquisition system for Korla fragrant pears. A: spectrometer; B: optical fiber; C: halogen lamp; D: sample; E: rotating stage; F: optical fiber bracket; G: lamp mounting plate; H: system mounting rack.

**Figure 6.**Scatter plot of the calibration set (×) and verification set (o) of stone cell content. (

**a**) SPA; (

**b**) MCUVE.

Sample Set | Numbers | Min (%) | Max (%) | Mean (%) | SD (%) | p |
---|---|---|---|---|---|---|

Cs | 90 | 0.240 | 0.657 | 0.486 | 0.100 | 0.008 |

Vs | 30 | 0.315 | 0.652 | 0.481 | 0.083 |

Frame Size | None | 3 | 5 | 7 | 9 | |
---|---|---|---|---|---|---|

Fitting Order | ||||||

none | 0.8613 | |||||

0.8214 | ||||||

1 | 0.8276 | 0.7867 | 0.7403 | 0.7012 | ||

0.8007 | 0.7616 | 0.7150 | 0.6710 | |||

2 | 0.8306 | 0.7928 | 0.7789 | |||

0.8035 | 0.7710 | 0.7458 | ||||

3 | 0.8414 | 0.8227 | 0.8023 | |||

0.8137 | 0.8006 | 0.7853 | ||||

4 | 0.8527 | 0.8419 | ||||

0.8195 | 0.8059 | |||||

5 | 0.8926 | 0.8647 | ||||

0.8210 | 0.8100 | |||||

6 | 0.8589 | |||||

0.8128 | ||||||

7 | 0.8527 | |||||

0.8026 |

Parameter | Preprocessing Algorithm | Factor Number | R_{C} | RMSE_{C} (%) | R_{V} | RMSE_{V} (%) |
---|---|---|---|---|---|---|

Stone cell content (%) | None | 9 | 0.8613 | 0.0360 | 0.8214 | 0.0412 |

MSC | 10 | 0.9191 | 0.0277 | 0.8879 | 0.0325 | |

SNV | 10 | 0.9189 | 0.0277 | 0.8935 | 0.0315 | |

S-G_{(7, 5)} | 10 | 0.8926 | 0.0319 | 0.8210 | 0.0409 | |

S-G_{(7, 5)}& MSC | 10 | 0.9001 | 0.0308 | 0.8614 | 0.0361 | |

S-G_{(7, 5)}& SNV | 10 | 0.8999 | 0.0308 | 0.8641 | 0.0356 |

_{C}: the correlation coefficient of the calibration set; RMSE

_{C}: root mean square error of the calibration set; R

_{V}: the correlation coefficient of the validation set; RMSE

_{V}: root mean square error of the validation set; S-G(7,5): Savitzky–Golay filter with a frame size of 7 and fitting order of 5.

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**MDPI and ACS Style**

Wang, T.; Zhang, Y.; Liu, Y.; Zhang, Z.; Yan, T.
Intelligent Evaluation of Stone Cell Content of Korla Fragrant Pears by Vis/NIR Reflection Spectroscopy. *Foods* **2022**, *11*, 2391.
https://doi.org/10.3390/foods11162391

**AMA Style**

Wang T, Zhang Y, Liu Y, Zhang Z, Yan T.
Intelligent Evaluation of Stone Cell Content of Korla Fragrant Pears by Vis/NIR Reflection Spectroscopy. *Foods*. 2022; 11(16):2391.
https://doi.org/10.3390/foods11162391

**Chicago/Turabian Style**

Wang, Tongzhao, Yixiao Zhang, Yuanyuan Liu, Zhijuan Zhang, and Tongbin Yan.
2022. "Intelligent Evaluation of Stone Cell Content of Korla Fragrant Pears by Vis/NIR Reflection Spectroscopy" *Foods* 11, no. 16: 2391.
https://doi.org/10.3390/foods11162391