Prediction of Peking duck intramuscle fat content by near-infrared spectroscopy

Qin, Fangli; Wang, Xinchun; Ding, Si-Ran; Li, Guang-Sheng; Hou, Zhuocheng

doi:10.1016/j.psj.2021.101281

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The selection of representative feature bands can reduce the size of the dataset without losing important information. The methods for feature band selection are stepwise discriminant analysis (SDA), the successive projection algorithm (SPA), the competitive adaptive reweighting algorithm (CARS), the genetic algorithm (GA), principal component analysis (PCA), random frog (RF), and Monte Carlo-uninformative variable elimination (MC-UVE) [ 57 , 58 , 59 , 60 , 61 , 62 , 63 ].…”

Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

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Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…Near-infrared spectroscopy ( NIRS ) is a rapid, nondestructive and accurate method for providing chemical composition, and has been applied in the prediction and evaluation of food and feed quality ( Gao et al, 2021 ; Noel et al, 2021 ; Oliveira et al, 2021 ; Qin et al, 2021 ). Previous research has confirmed the capability of NIRS to predict the protein content in pork, beef, commercial broilers, egg, and insect ( Barbin et al, 2013 ; Su et al, 2014 ; Zhao et al, 2018 ; Patel et al, 2021 ; Cruz-Tirado et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…These could result in the differences in chemical contents ( Xiao et al, 2021 ; Chaiwang et al, 2023 ). Previous research reported that the more the range of the chemical content in the sample, the more accurate the model ( Su et al, 2014 ; Qin et al, 2021 ). Therefore, the native chickens with different body sizes, including large-, medium-, and small-sized, were used for calibration and validation models to improve the prediction performance.…”

Section: Introductionmentioning

confidence: 99%