Predicting the nutrition deficiency of fresh pear leaves with a miniature near-infrared spectrometer in the laboratory

Jin, Xiu; Wang, Lianglong; Zheng, W.; Zhang, Xiaodan; Liu, Li; Li, Shaowen; Rao, Yuan; Xuan, Jinxiang

doi:10.1016/j.measurement.2021.110553

Cited by 22 publications

(5 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By using a NIR-M-F1-C spectrometer to analyze the samples, the raw NIR spectra of the chrysanthemum tea varieties could be obtained. However, the direction of light changes due to the effect of small inhomogeneity on the surface of chrysanthemum tea when collecting spectral data, and the noise-generated scatter may affect the raw NIR spectra [40], and, therefore, preprocessing the spectral data is important for the subsequent processing of the NIR spectra. In this experiment, several preprocessing algorithms were applied to pretreat the NIR spectral data, including standard normal variation (SNV), multiplicative scattering correction (MSC), Savitsky-Golay (SG) filtering [41], and mean centering (MC), which improved the spectral data.…”

Section: Preprocessingmentioning

confidence: 99%

Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties

Zhang,

Wu,

et al. 2024

Foods

View full text Add to dashboard Cite

The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.

show abstract

Section: Preprocessingmentioning

confidence: 99%

Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties

Zhang,

Wu,

et al. 2024

Foods

View full text Add to dashboard Cite

show abstract

“…In this experiment, a reflective miniature handheld near-infrared spectrometer was used to acquire 228 bands in the wavelength range of 900-1700 nm, with a spectral resolution of 3.89 nm. The signal-to-noise ratio was 5000:1 [17][18][19].…”

Section: Near-infrared Spectral Data Acquisitionmentioning

confidence: 99%

Quantitatively Determine the Iron Content in the Fruit of ‘Huangguan’ Pear Using Near-Infrared Spectroscopy

Fang

et al. 2023

Horticulturae

Self Cite

View full text Add to dashboard Cite

‘Huangguan’ pear has excellent quality, strong adaptability, and good socioeconomic value. Iron is one of the important trace elements in plants, and iron imbalance seriously affects the growth and development of pear trees and reduces their economic benefits. If the iron content in pear fruit can be easily and non-destructively detected using modern technology during the critical period of fruit development, it will undoubtedly help guide actual production. In this study, ‘Huangguan’ pear fruit was used as the research object, and the possibility of using the more convenient near-infrared spectroscopy (900~1700 nm) technology for nondestructive detection of the iron content in the peel and pulp of ‘Huangguan’ pear was explored. First, 12 algorithms were used to preprocess the original spectral data, and based on the original and the preprocessed spectral data, partial least squares regression and gradient boosting regression tree algorithms were used. A full-band prediction model of the iron content in the peel and pulp of ‘Huangguan’ pear was established, and the genetic algorithm was used to extract characteristic wavelengths, establish a characteristic wavelength prediction model, and evaluate the prediction effect of each model according to the coefficient of determination R² and the relative analysis error RPD. After comparison, we found that the prediction model with the best prediction of the iron content in the peel and pulp of ‘Huangguan’ pear reaches class A, and the prediction effect is good and meets expectations. This experiment shows that the use of near-infrared spectroscopy can achieve better prediction of the iron content in the peel and pulp of ‘Huangguan’ pear.

show abstract

“…In recent years, the application of near-infrared spectroscopy became increasingly mature in the field of agricultural product quality testing because of its nonpolluting simple operation and lack of damage to the sample during the testing process [8][9][10]. In our team's research, WenJing Ba and Lianglong Wang modelled and analysed the nutritional deficiencies in pear leaves and Fusarium head blight in wheat grains using near-infrared spectroscopy [11][12][13], accumulating experience for other team members in modelling and analysing using near-infrared spectroscopy. Therefore, this paper uses near-infrared spectroscopy to establish an identification model for DPWD and discusses the feasibility of using near-infrared spectroscopy for the diagnosis of DPWD.…”

Section: Introductionmentioning

confidence: 99%

The Nondestructive Model of Near-Infrared Spectroscopy with Different Pretreatment Transformation for Predicting “Dangshan” Pear Woolliness Disease

et al. 2023

Self Cite

View full text Add to dashboard Cite

The “Dangshan” pear woolliness response is a physiological disease that mostly occurs in the pear growth process. The appearance of the disease is not obvious, and it is difficult to detect with the naked eye. Therefore, finding a way to quickly and nondestructively identify “Dangshan” pear woolliness disease is of great significance. In this paper, the near-infrared spectral (NIR) data of “Dangshan” pear samples were collected at 900–1700 nm reflectance spectra using a handheld miniature NIR spectrometer, and the data were modelled and analysed using random forest (RF), support vector machine (SVM) and boosting algorithms under the processing of 24 pretreatment methods. Considering the variations between different pretreatment methods, this work determined the relative optimality index of different pretreatment methods by evaluating their effects on model accuracy and Kappa and selected the best-performing first derivative with standard normal variate and Savitzky–Golay and first derivative with multiplicative scatter correction and Savitzky–Golay as the best pretreatment methods. With the best pretreatment method, all five models in the three categories showed good accuracy and stability after parameter debugging, with accuracy and F1 greater than 0.8 and Kappa floating at approximately 0.7, reflecting the good classification ability of the models and proving that near-infrared spectroscopy (NIRS) in the rapid identification of “Dangshan” pear woolliness response disease was feasible. By comparing the performance differences of the models before and after the pretreatment methods, it was found that the ensemble-learning models such as RF and boosting were more stringent on pretreatment methods in identifying “Dangshan” pear woolliness response disease than support vector machines, and the performance of the ensemble learning models was significantly improved under appropriate pretreatment methods. This experiment provided a relatively stable detection method for “Dangshan” pear woolliness response disease under nonideal detection conditions by analysing the impact of pretreatment methods and models on the prediction result.

show abstract

Predicting the nutrition deficiency of fresh pear leaves with a miniature near-infrared spectrometer in the laboratory

Cited by 22 publications

References 56 publications

Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties

Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties

Quantitatively Determine the Iron Content in the Fruit of ‘Huangguan’ Pear Using Near-Infrared Spectroscopy

The Nondestructive Model of Near-Infrared Spectroscopy with Different Pretreatment Transformation for Predicting “Dangshan” Pear Woolliness Disease

Contact Info

Product

Resources

About