Online determination of coffee roast degree toward controlling acidity

2020 ◽  
Vol 28 (4) ◽  
pp. 175-185
Author(s):  
Nathan Yergenson ◽  
D Eric Aston
Keyword(s):  
Near Infrared ◽  
Color Space ◽  
Visible Spectrum ◽  
Titratable Acidity ◽  
Least Squares Regression ◽  
L Value ◽  
Linear Effects ◽  
The Common ◽  
Roasting Temperature

Three methods of measuring coffee roast degree were compared using titratable acidity as an indicator of roast-dependent flavor change. The first roast degree method was based on prediction of the cracks with online near infrared spectroscopy and partial least squares regression, the second was based on changes in online near infrared absorbance, and the third was the common L* value from the CIELAB color space in the visible spectrum. Roasting trials utilized arabica coffee from eight origins in an air roaster, and results demonstrated the superiority of an online near infrared sensor for real-time roast degree measurement. A second dataset with constant temperature roasts showed how acidity can be controlled by changing both the roasting temperature and roast degree, finding the linear effects of roast time and roast degree on acidity.

scholarly journals Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR

2019 ◽  
Vol 1 (2) ◽  
pp. 246-256
Author(s):  
Benjamaporn Matulaprungsan ◽  
Chalermchai Wongs-Aree ◽  
Pathompong Penchaiya ◽  
Phonkrit Maniwara ◽  
Sirichai Kanlayanarat ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Sample Type ◽  
Least Squares Regression ◽  
Calibration Equation ◽  
E Coli ◽  
Total Bacteria

Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.

DWT-PLS Regression on Near-Infrared Spectra for Moisture and Volatile Determination of Coal

2013 ◽  
Vol 848 ◽  
pp. 313-316
Author(s):  
Xiao Li Yang ◽  
Fan Wang ◽  
Ji Shu Chen ◽  
Gong Zhe Ma
Keyword(s):  
Partial Least Squares Regression ◽  
Bituminous Coal ◽  
Near Infrared ◽  
Parameters Optimization ◽  
Discrete Wavelet ◽  
Pls Regression ◽  
Least Squares Regression ◽  
Satisfactory Performance ◽  
Near Infrared Spectra

We studied moisture and volatile determination in bituminous coal samples using near-infrared (NIR) spectra. This research was developted by applying partial least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, NIR spectra were pre-processed by DWT for fitting and compression. Then, DWT coefficients were used to build regression model with PLS. We used NIR spectra to determination moisture and volatile determination in coal samples seperately and simultaneously. Through parameters optimization, the results show that DWT-PLS can obtain satisfactory performance for separate and simultanous determination.

scholarly journals Estimate of the density of Eucalyptus grandis W. Hill ex Maiden using near infrared spectroscopy

CERNE ◽  
2013 ◽  
Vol 19 (4) ◽  
pp. 647-652 ◽  
Author(s):  
Silviana Rosso ◽  
Graciela Ines Bolzon de Muniz ◽  
Jorge Luis Monteiro de Matos ◽  
Clóvis Roberto Haselein ◽  
Paulo Ricardo Gherardi Hein ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Eucalyptus Grandis ◽  
Basic Density ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Old Trees

This study aimed to analyze use of near infrared spectroscopy (NIRS) to estimate wood density of Eucalyptus grandis. For that, 66 27-year-old trees were logged and central planks were removed from each log. Test pieces 2.5 x 2.5 x 5.0 cm in size were removed from the base of each plank, in the pith-bark direction, and subjected to determination of bulk and basic density at 12% moisture (dry basis), followed by spectral readings in the radial, tangential and transverse directions using a Bruker Tensor 37 infrared spectrophotometer. The calibration to estimate wood density was developed based on the matrix of spectra obtained from the radial face, containing 216 samples. The partial least squares regression to estimate bulk wood density of Eucalyptus grandis provided a coefficient of determination of validation of 0.74 and a ratio performance deviation of 2.29. Statistics relating to the predictive models had adequate magnitudes for estimating wood density from unknown samples, indicating that the above technique has potential for use in replacement of conventional testing.

Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectances: a comparison of statistical methods

1996 ◽  
Vol 26 (4) ◽  
pp. 590-600 ◽  
Author(s):  
Katherine L. Bolster ◽  
Mary E. Martin ◽  
John D. Aber
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Data Sets ◽  
Independent Data ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Regression Methods

Further evaluation of near infrared reflectance spectroscopy as a method for the determination of nitrogen, lignin, and cellulose concentrations in dry, ground, temperate forest woody foliage is presented. A comparison is made between two regression methods, stepwise multiple linear regression and partial least squares regression. The partial least squares method showed consistently lower standard error of calibration and higher R2 values with first and second difference equations. The first difference partial least squares regression equation resulted in standard errors of calibration of 0.106%, with an R2 of 0.97 for nitrogen, 1.613% with an R2 of 0.88 for lignin, and 2.103% with an R2 of 0.89 for cellulose. The four most highly correlated wavelengths in the near infrared region, and the chemical bonds represented, are shown for each constituent and both regression methods. Generalizability of both methods for prediction of protein, lignin, and cellulose concentrations on independent data sets is discussed. Prediction accuracy for independent data sets and species from other sites was increased using partial least squares regression, but was poor for sample sets containing tissue types or laboratory-measured concentration ranges beyond those of the calibration set.

scholarly journals Direct Determination of Moisture in Powder Milk Using Near Infrared Spectroscopy

2006 ◽  
Vol 2006 ◽  
pp. 1-4 ◽  
Author(s):  
R. Nagarajan ◽  
Parul Singh ◽  
Ranjana Mehrotra
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Milk Powder ◽  
Direct Determination ◽  
Wavelength Region ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Karl Fischer

Moisture content in commercially available milk powder was investigated using near infrared (NIR) diffuse reflectance spectroscopy with an Indian low-cost dispersive NIR spectrophotometer. Different packets of milk powder of the same batch were procured from the market. Forty-five samples with moisture range 4–10% were prepared in the laboratory. Spectra of the samples were collected in the wavelength region 800–2500 nm. Moisture values of all the samples were simultaneously determined by Karl Fischer (KF) titration. These KF values were used as reference for developing calibration model using partial least squares regression (PLSR) method. The calibration and validation statistics areR cal2:0.9942,RMSEC:0.1040, andR val2:0.9822,RMSEV:0.1730. Five samples of unknown moisture contents were taken for NIR prediction using developed calibration model. The agreement between NIR predicted results and those of Karl Fischer values is appreciable. The result shows that the instrument can be successfully used for the determination of moisture content in milk powder.

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