Fast discrimination of chocolate varieties using near infrared spectroscopy

Zhang, Bin; Deng, Lisheng; Gao, Qiao Zhuo; Wu, Xinyu; Xu, Yangsheng

doi:10.1109/ical.2008.4636245

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NIRS has been widely used in agriculture, food analysis, crops quality analysis, petrochemical industry, pharmaceutical industry, textile industry, bio-medicine, mineralogy, etc. [1]- [4]. The applications of NIRS in the field of agriculture and plants have been a hot topic, but its use in the classification of plants is still rare.…”

Section: Introductionmentioning

confidence: 99%

Application of NIR Spectroscopy in Classification of Plant Species

Guo

Lin

Wang

et al. 2009

2009 First International Workshop on Education Technology and Computer Science

View full text Add to dashboard Cite

Near-infrared spectroscopy (NIRS) analysis is a rapidly-developing non-destructive technology with increasingly wide range of applications, but application of NIRS technology in the classification of plants is still rare. Based on experimental study, this paper uses the approach of principal component analysis (PCA) combined with the establishment of a Mahalanobis distance of plants' leaves to establish the discrimination model of leaves, and adopts multiplicative scatter correction (MSC) and a first-order differential to analyze the model. The analysis results are quite consistent with experimental data, and obtained a satisfactory classification rate of 100%, which shows that this approach can be used to classify plant species, and it can become a very useful tool in many relevant fields.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of NIR Spectroscopy in Classification of Plant Species

Guo

Lin

Wang

et al. 2009

2009 First International Workshop on Education Technology and Computer Science

View full text Add to dashboard Cite

show abstract

Discrimination of centre composition in panned chocolate goods using near infrared spectroscopy

Johnson

2022

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Non-destructively identifying the centre composition of panned chocolate goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, nearinfrared spectra (1000–2500 nm) were collected from chocolate-coated peanuts and chocolate-coated sultanas ( n = 170 of each) in order to investigate the prospect of non-invasively detecting the composition of the centre. Principal component analysis confirmed that the spectra of these samples were distinct from one another. The partial least squares discriminant analysis (PLS-DA) model showed a high level of separation between chocolate-coated peanuts and sultanas in the training set (R2 = 0.95; RPD = 4.4). Discrimination between peanut and sultana samples from an independent test set was also possible, although with slightly less distinct separation between the sample types. A soft independent modelling by class analogy model was also able to differentiate between the two sample types, albeit with higher levels of misclassification compared to PLS-DA. Incorporating samples from different manufacturers may be useful for improving the broader applicability of the model.

show abstract

Fast discrimination of chocolate varieties using near infrared spectroscopy

Cited by 2 publications

References 16 publications

Application of NIR Spectroscopy in Classification of Plant Species

Application of NIR Spectroscopy in Classification of Plant Species

Discrimination of centre composition in panned chocolate goods using near infrared spectroscopy

Contact Info

Product

Resources

About