Rapid screening and quantitative analysis of adulterant Lonicerae Flos in Lonicerae Japonicae Flos by Fourier-transform near infrared spectroscopy

Zhao, Jing; Cui, Pengdi; Liu, Huan; Wang, Chunhua; Liu, Ming; Li, Gang; Pan, Mengyun; Li, Zheng; Suo, Tongchuan

doi:10.1016/j.infrared.2019.103139

Cited by 20 publications

(5 citation statements)

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“…These techniques are also known as machine learning in the field of computer science. The combination of NIR spectroscopy and machine learning showed successful results for monitoring food adulteration in previous research such as honey [2][3][4][5], milk [6], pepper [7], sesame oil [8], Lonicerae Japonicae Flos [9], soybean oil [10], Panax notoginseng [11] and notoginseng [12]. However, the NIR spectrum consists of a great number of absorbance values on all wavenumber range reaching thousands of variables.…”

Section: Introductionmentioning

confidence: 99%

Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya

et al. 2020

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This research aimed to study the combination of NIR spectroscopy and machine learning for monitoring chilli sauce adulterated with papaya smoothie. The chilli sauce was produced by the famous community enterprise of chilli sauce processing in Thailand. The ingredients of the chilli sauce consisted of 45% chilli, 25% sugar, 20% garlic, 5% vinegar, and 5% salt. The chilli sauce sample was mixed with ripened papaya (Khaek Dam variety) smoothie with 9 levels from 10 to 90 %w/w. The NIR spectra of pure chilli sauce, papaya smoothie and 9 adulterated chilli sauce samples were recorded using FT-NIR spectrometer in the wavenumber range of 12500 and 4000 cm-1. Three machine learning algorithms were applied to develop a model for monitoring adulterated chilli sauce, including partial least squares regression (PLS), support vector machine (SVM), and backpropagation neural network (BPNN). All model presented performance of prediction in the validation set with R2al = 0.99 while RMSEP of PLS, SVM and BPNN were 1.71, 2.18 and 3.27% w/w respectively. This finding indicated that NIR spectroscopy coupled with machine learning approaches were shown to be an alternative technique to monitor papaya smoothie adulterated in chilli sauce in the global food industry.

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Section: Introductionmentioning

confidence: 99%

Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya

et al. 2020

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“…The inferiority of unsupervised methods as indicated in either MIR or NIR spectroscopy ( Liu et al., 2019 ; Yang et al., 2020 ) due to poor capability to extract effective information. Data pre-processing, variables selection, IR spectroscopic tri-step identification approach ( Chen et al., 2015 ; Chen et al., 2018 ), and more supervised algorithms ( Shao et al., 2016 ; Chen et al., 2019 ; Zhao et al., 2019 ), which aimed at reducing uncorrelated spectral information, were studied to reach satisfactory results, from adulterated binary samples to adulterated quaternary samples ( Nie et al., 2013 ; Liu et al., 2019 ). We notice that it is difficult to transfer the models based on small samples directly to other samples because of the limitations of the representativeness of small samples and analytical techniques, as well as the various presence of adulterated chemical components ( Li et al., 2020 ).…”

Section: Quality and Safety Inspection Of Herbal Raw Materialsmentioning

confidence: 99%

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

et al. 2023

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Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

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“…The correlation between the modern and traditional applications of LJF remains ambiguous 1 . Apart from medicinal use, LJF is extensively employed in the Chinese food industry as a fundamental aromatic component in teas and beverages 10,11 . Furthermore, LJF is extensively utilized in the cosmetics, perfumery, and pharmaceutical sectors 2,12 .…”

Section: Introductionmentioning

confidence: 99%

“…1 Apart from medicinal use, LJF is extensively employed in the Chinese food industry as a fundamental aromatic component in teas and beverages. 10,11 Furthermore, LJF is extensively utilized in the cosmetics, perfumery, and pharmaceutical sectors. 2,12 Due to the lower production of LJF in comparison to LF, the price of LJF is comparatively higher.…”

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confidence: 99%

Discrimination and characterization of volatile organic compounds in Lonicerae Japonicae flos and Lonicerae flos using multivariate statistics combined with headspace gas chromatography–ion mobility spectrometry and headspace solid‐phase microextraction gas chromatography–mass spectrometry techniques

Wu,

Yin,

et al. 2024

Rapid Comm Mass Spectrometry

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RationaleThe volatile organic compounds (VOCs) of Lonicerae Japonicae flos (LJF) and Lonicera flos (LF) play a pivotal role in determining their sensory characteristics, medicinal properties, and subsequent impact on market pricing and consumer preferences. However, the differences and specificity of these VOCs remain obscure. Hence, it is crucial to conduct a comprehensive characterization of the VOCs in LJF and LF and pinpoint their potential differential VOCs.MethodsIn this study, headspace gas chromatography–ion mobility spectrometry (HS‐GC/IMS) and headspace solid‐phase microextraction gas chromatography–mass spectrometry (HS‐SPME‐GC/MS) were employed to comprehensively investigate the compositional characteristics and distinctions in VOCs between LJF and LF. Multivariate statistical analysis was used to identify candidate differential VOCs of LJF and LF samples.ResultsA total of 54 and 88 VOCs were identified using HS‐GC/IMS and HS‐SPME‐GC/MS analysis, respectively. Primary VOCs detected in LJF include leaf alcohol, (E)‐2‐hexen‐1‐ol dimer, 2‐octyn‐1‐ol, and (E)‐3‐hexen‐1‐ol. Key VOCs prevalent in LF encompass farnesol, heptanoic acid, octanoic acid, and valeric acid. Multivariate statistical analysis indicates that compounds such as phenethyl alcohol and leaf alcohol were selected as potential VOCs for distinguishing between LJF and LF.ConclusionThis research conducted a comprehensive analysis of the fundamental volatile components in both LJF and LF. It subsequently elucidated the distinctions and specificities within their respective VOC profiles. And this study enables differentiation between LJF and LF through the analysis of VOCs, offering valuable insights for enhancing the quality control of both LJF and LF.

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Rapid screening and quantitative analysis of adulterant Lonicerae Flos in Lonicerae Japonicae Flos by Fourier-transform near infrared spectroscopy

Cited by 20 publications

References 50 publications

Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya

Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

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