Classification of wolfberry from different geographical origins by using electronic tongue and deep learning algorithm

Yang, Zhengwei; Wang, Zhiqiang; Yuan, Wenhao; Li, Caihong; Jing, Xiaoyu; Han, Hao

doi:10.1016/j.ifacol.2019.12.592

Cited by 11 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang, Wang, et al. (2019) combined VE‐tongue with deep learning algorithms to discriminate between dried berries from four different regions in China. Best results for origin classification were achieved with a Convolutional Neural Network at 98.27% accuracy.…”

Section: Electronic Sensorsmentioning

confidence: 99%

Authentication of berries and berry‐based food products

Salo

Nguyen

Alakärppä

et al. 2021

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Berries represent one of the most important and high-valued group of modernday health-beneficial "superfoods" whose dietary consumption has been recognized to be beneficial for human health for a long time. In addition to being delicious, berries are rich in nutrients, vitamins, and several bioactive compounds, including carotenoids, flavonoids, phenolic acids, and hydrolysable tannins. However, due to their high value, berries and berry-based products are often subject to fraudulent adulteration, commonly for economical gain, but also unintentionally due to misidentification of species. Deliberate adulteration often comprises the substitution of high-value berries with lower value counterparts and mislabeling of product contents. As adulteration is deceptive toward customers and presents a risk for public health, food authentication through different methods is applied as a countermeasure. Although many authentication methods have been developed in terms of fast, sensitive, reliable, and low-cost analysis and have been applied in the authentication of a myriad of food products and species, their application on berries and berry-based products is still limited. The present review provides an overview of the development and appli-

show abstract

Section: Electronic Sensorsmentioning

confidence: 99%

Authentication of berries and berry‐based food products

Salo

Nguyen

Alakärppä

et al. 2021

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…In spite of the subjectivity of the methods, it is frequent to discriminate the wolfberries through color, shape, odor, taste, and other qualities highly dependent on the personal elicited sensorial impressions. For this reason, several methods have been developed to overcome this subjective factor, such as voltametric electronic tongue [10]; electronic tongue (coupled combined with deep learning algorithm (convolutional neural network) [11]; mineral profile with chemometric approaches [12]; two-dimensional correlation spectroscopy combined with deep learning algorithm, such as convolutional neural network [13]; surface near infrared spectra combined with multi-class support vector machine [14]; Fourier transform infrared spectroscopy combined with artificial neural networks [15]; carbon isotope analysis of specific volatile compounds (e.g., limonene, tetramethylpyrazine, safranal, geranyl acetone, and β-ionone) by gas chromatography-combustion isotope ratio mass spectrometry with headspace-solid phase microextraction [8]; stable isotopes, earth elements, free amino acids, and saccharides using orthogonal projection to latent structure-discriminant analysis [16]; non-targeted liquid chromatography coupled to quadrupole time-of-flight mass spectrometry combined with statistical analysis [17] are some examples.…”

Section: Introductionmentioning

confidence: 99%

Chemical and Biological Properties of Three Poorly Studied Species of Lycium Genus—Short Review

Miguel

2022

Metabolites

View full text Add to dashboard Cite

The genus Lycium belongs to the Solanaceae family and comprises more than 90 species distributed by diverse continents. Lycium barbarum is by far the most studied and has been advertised as a “superfood” with healthy properties. In contrast, there are some Lycium species which have been poorly studied, although used by native populations. L. europaeum, L. intricatum and L. schweinfurthii, found particularly in the Mediterranean region, are examples of scarcely investigated species. The chemical composition and the biological properties of these species were reviewed. The biological properties of L. barbarum fruits are mainly attributed to polysaccharides, particularly complex glycoproteins with different compositions. Studies regarding these metabolites are practically absent in L. europaeum, L. intricatum and L. schweinfurthii. The metabolites isolated and identified belong mainly to polyphenols, fatty acids, polysaccharides, carotenoids, sterols, terpenoids, tocopherols, and alkaloids (L. europaeum); phenolic acids, lignans, flavonoids, polyketides, glycosides, terpenoids, tyramine derivatives among other few compounds (L. schweinfurthii), and esters of phenolic acids, glycosides, fatty acids, terpenoids/phytosterols, among other few compounds (L. intricatum). The biological properties (antioxidant, anti-inflammatory and cytotoxic against some cancer cell lines) found for these species were attributed to some metabolites belonging to those compound groups. Results of the study concluded that investigations concerning L. europaeum, L. intricatum and L. schweinfurthii are scarce, in contrast to L. barbarum.

show abstract