scholarly journals Identification of fresh and expired ground roasted robusta coffee using UV-visible spectroscopy and chemometrics

2018 ◽  
Vol 197 ◽  
pp. 09003 ◽  
Author(s):  
Meinilwita Yulia ◽  
Diding Suhandy
Keyword(s):  
Moving Average ◽  
Principal Component ◽  
Analysis Data ◽  
Visible Spectroscopy ◽  
Roasted Coffee ◽  
Classification Rate ◽  
Robusta Coffee ◽  
Standard Normal Variate ◽  
Smoothing Window ◽  
Uv Visible

The freshness of ground roasted coffee escapes extremely fast. For this reason, the evaluation of conservation state of ground roasted coffee must be taken into account for acceptability of coffee. Unfortunately, it is difficult to discriminate the fresh and expired ground roasted coffee physically by our naked eyes. Thus, it is desired to develop an analytical method to evaluate the fresh and expired ground roasted coffee using reliable methods. The objective of this research was to evaluate the potential of UV-visible spectroscopy and chemometrics method for classification of fresh and expired ground roasted robusta coffee. A number of 200 samples of robusta fresh coffee and 200 samples of robusta expired coffee was used. The spectral data were pre-treated using standard normal variate (SNV), moving average smoothing (window: 9) and Savitzky-Golay 2nd derivative (order: 2; window: 11). The analysis data was done statistically using multivariate chemometric techniques, including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) in the spectral range of 230-400 nm. PCA with PC1 = 94% and PC2 = 4% showed clear clustering of samples (p ≤ 0.05). UV-visible spectroscopy with SIMCA analysis allowed to classify between fresh and expired ground roasted robusta coffee with a correct classification rate of 100%.

scholarly journals Klasifikasi Madu Berdasarkan Jenis Lebah (Apis dorsata versus Apis mellifera) Menggunakan Spektroskopi Ultraviolet dan Kemometrika

2020 ◽  
Vol 25 (4) ◽  
pp. 564-573
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia ◽  
Kusumiyati Kusumiyati
Keyword(s):  
Apis Mellifera ◽  
Phenolic Compounds ◽  
Spectral Data ◽  
Uv Spectroscopy ◽  
Low Cost ◽  
Moving Average ◽  
Apis Dorsata ◽  
Classification Rate ◽  
Standard Normal Variate ◽  
Standard Normal

In this research, spectral data in UV region (200-400 nm) alongside PCA and SIMCA chemometrics were used to classify two types of honey obtained from different honeybees (Apis dorsata versus Apis mellifera). A total of 200 Durian monofloral honey samples from Apis dorsata and 120 samples for Longan monofloral honey from Apis mellifera were prepared. Therefore, spectral data were recorded based on the following parameters: range of acquisition 200-400 nm, transmittance mode, and interval 1 nm. In addition, the original spectra were transformed using three different algorithms: moving average smoothing with 11 segments, standard normal variate (SNV), and Savitzky-Golay 1st derivative with 11 segments and 2 ordos. The result of PCA using transformed spectra in the range of 250-400 nm explained the possibility of clearly separating Durian and Longan honey along the PC1 axis, with 98% variance, while the SIMCA showed a 100% proper classification rate for all prediction samples. In addition, several important wavelengths were identified alongside high x-loadings values at 270 and 300 nm. These results were closely related to the absorbance of important phenolic compounds in honey, including benzoic, salicylic, and aryl-alyphatic acids. The results demonstrate a probability to establish simple and low-cost honey authentication systems, using UV spectroscopy and chemometrics on free-chemical in sample preparations. Keywords: authentication, Apis dorsata, Apis mellifera, SIMCA, UV spectroscopy

scholarly journals Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 61
Author(s):  
Cinthia de Carvalho Couto ◽  
Otniel Freitas-Silva ◽  
Edna Maria Morais Oliveira ◽  
Clara Sousa ◽  
Susana Casal
Keyword(s):  
Central America ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Roasted Coffee ◽  
Arabica Coffee ◽  
Robusta Coffee ◽  
Geographical Discrimination ◽  
Geographic Regions ◽  
Non Destructive

Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis—PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America).

scholarly journals Facile Detection of Oil Adulteration using UV-Visible Spectroscopy Coupled with Chemometrics Analysis

2021 ◽  
Vol 6 (1) ◽  
pp. 14
Author(s):  
Nina Gusti ◽  
Dinda Oktarina ◽  
Rina Elvia ◽  
Euis Nursa’adah ◽  
Rendy W Wardhana ◽  
...  
Keyword(s):  
Principal Component ◽  
Good Prediction ◽  
Compound Identification ◽  
Visible Spectroscopy ◽  
Promising Alternative ◽  
Principle Component ◽  
Used Oil ◽  
Lubricant Oil ◽  
Principle Component Regression ◽  
Uv Visible

Engine and machine oils, better known as lubricant, is a fast-moving part within the motorcycle and automobile industries. Due to its high demand, these oils are often counterfeited by irresponsible people to get more profit. The thing most often done to adulterate oil is by mixing it with other low-quality or used oil. Here, we propose a simple analytical method to identify oil adulteration by using UV-Visible spectroscopy. A number of 425 genuine and adulterated oils were used as samples. After appropriate dilution using n-hexane, the samples were analyzed by UV-Visible spectrophotometer followed by Principle Component Analysis (PCA) and Principle Component Regression (PCR) as part of the chemometrics analysis. The results show that prediction samples were accurately classified into their corresponding groups with PCA scores of 49% and 27% for principal component 1 and 2, respectively. PLS model achieved a good prediction to detect lubricant oil adulteration, with R-Square of predicted and reference samples were 0.9257 and 0.9204, respectively. The proposed method shows a promising alternative to the conventional chemical method using a more sophisticated instruments such as GC-MS and HPLC for oil or other organic compound identification.

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