Authentication of Whole and Ground Coffee Beans by near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Gerard Downey ◽  
Jerôme Boussion ◽  
Dominique Beauchêne
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Correct Identification ◽  
Correct Classification Rate ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Identification Rate ◽  
Coffee Beans ◽  
Correct Identification Rate ◽  
Nir Reflectance Spectroscopy

The potential of NIR reflectance spectroscopy for discriminating between pure Arabica and pure Robusta coffees and blends of these two was investigated. Studies were performed on whole and ground beans using a factorial discriminant procedure. For whole beans, in the absence of blended samples, a correct classification rate of 96.2% was achieved. Inclusion of blended samples reduced this figure to between 82.9 and 87.6%. In the case of ground samples, including blends, a correct identification rate of 83.02% was achieved. The molecular basis for discrimination is discussed.

Use of near Infrared Reflectance Spectroscopy to Analyse Bovine Faecal Samples

2002 ◽  
Vol 10 (4) ◽  
pp. 309-314 ◽  
Author(s):  
D. Cozzolino ◽  
A. La Manna ◽  
D. Vaz Martins
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Dry Weight ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Faecal Samples ◽  
Acid Detergent Fibre ◽  
Nir Reflectance Spectroscopy ◽  
Calibration Equations

Near infrared (NIR) reflectance spectroscopy was used to predict nitrogen (N), acid detergent fibre (ADF), neutral detergent fibre (NDF) and chromium (Cr) in beef faecal samples. One hundred and twenty faecal samples were scanned in a NIRSystems 6500 monochromator instrument over the wavelength range of 400–2500 nm in reflectance. Calibration equations were developed using modified partial least squares (MPLS) with internal cross validation to avoid overfitting. The coefficient of determination in calibration ( R2cal) and the standard error in cross validation ( SECV) were 0.80 (0.74) for N, 0.92 (12.04) for ADF, 0.86 (13.5) for NDF and 0.56 (0.07) for Cr in g kg−1 dry weight, respectively. Results for validation were 0.78 ( SEP: 0.1) for N, 0.74 ( SEP: 7.5) for ADF, 0.85 ( SEP: 8.5) for NDF and 0.10 (0.09) for Cr in g kg−1 dry weight, respectively.

scholarly journals Aplikasi NIRS dan Principal Component Analysis (PCA) untuk Mendeteksi Daerah Asal Biji Kopi Arabika (Coffea arabica)

2016 ◽  
Vol 1 (1) ◽  
pp. 954-960
Author(s):  
Syahrul Ramadhan ◽  
Agus Arip Munawar ◽  
Diswandi Nurba
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Coffee Beans

Abstrak. Kopi merupakan spesies tanaman berbentuk pohon yang termasuk dalam famili Rubiaceae dan genus Coffea, tumbuh tegak, bercabang dan bila dibiarkan dapat tumbuh mencapai tinggi 12 meter. Pendeteksian mutu pangan yang cepat dan efisien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Sebanyak 54 sampel biji kopi diambil dari 6 Provinsi yang berbeda, yaitu: Aceh, Bali, Bengkulu, Nusa Tenggara Barat, Jawa Barat dan Jawa Timur. Pengamatan meliputi Principal Component Analysis (PCA) sebagai metode klasifikasi dan Pretreatment Multiplicative Scatter Correction (MSC) sebagai metode koreksi spektrum. Hasil pengujian menunjukkan bahwa PCA hanya mampu mengklasifikasikan biji kopi dari Provinsi Aceh dan Provinsi Jawa Timur, sedangkan dengan penambahan Pretreatment MSC mampu mengklasifikasikan biji kopi dari Provinsi Aceh dan Provinsi Bali dengan tingkat keberhasilan 100%.Abstract. Coffee is belong to family Rubiaceae and the genus Coffea, grow upright, branched, and can grow up to 12 meters high. The detection of food quality quickly and efficiently can be realized through the development of Near Infrared Reflectance Spectroscopy (NIRS) technology. A total of 54 Coffee bean samples were taken from 6 different province, namely: Aceh, Bali, Bengkulu, West Nusa Tenggara, West Java and East Java. Data analysis included Principal Component Analysis (PCA) were used to classify coffee based on geographic origin. Multiplicative Scatter Correction (MSC) method was used as spectra correction. The results shows that PCA is able to classify coffee beans from the Aceh and East Java province, while the addition of MSC Pretreatment able to classify the coffee beans from the province of Aceh and Bali province with 100% success rate.

Predicting Elements in White Pine and Red Oak Foliage with Visible-Near Infrared Reflectance Spectroscopy

1997 ◽  
Vol 5 (2) ◽  
pp. 77-82 ◽  
Author(s):  
R.A. Hallett ◽  
J.W. Hornbeck ◽  
M.E. Martin
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Red Oak ◽  
Acid Digestion ◽  
White Pine ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy ◽  
Calibration Equations ◽  
Mn Concentrations

Near infrared (NIR) reflectance spectroscopy was evaluated for its effectiveness at predicting Al, Ca, Fe, K, Mg and Mn concentrations in white pine ( Pinus strobus L.) and red oak ( Quercus rubra L.) foliage. A NIR spectrophotometer was used to scan 470 dried, ground foliage samples. These samples were used to develop calibration equations using a modified partial least squares (MPLS) regression technique. For the calibration equations, concentrations of Al, Ca, Fe, K, Mg and Mn as determined by acid digestion and laboratory analysis were regressed against second-difference absorbance values measured from 400 to 2498 nm. The regression models developed by NIR reflectance spectroscopy were unable to predict Fe. Predictions were satisfactory for Al, Ca, K, Mn and Mg. It still is uncertain which mineral/organic associations are being detected by NIR reflectance spectroscopy. Future applications may include prediction of element concentrations in the forest canopy via remote sensing.

Commercial Wheatflour Authentication by Discriminant Analysis of near Infrared Reflectance Spectra

1993 ◽  
Vol 1 (4) ◽  
pp. 187-197 ◽  
Author(s):  
A. Sirieix ◽  
G. Downey
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Correct Classification ◽  
Infrared Reflectance ◽  
Correct Classification Rate ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Discriminant Model ◽  
Flour Milling

This paper reports an application of qualitative analysis in the flour milling industry based on near infrared spectroscopy and a factorial discriminant procedure. Samples of different commercial flour types were collected from a number of mills and a discriminant model developed; evaluation of this model on a different set of 99 samples produced a correct classification rate of 97%.

The Potential of near Infrared Reflectance Spectroscopy as a Tool for the Chemical Characterisation of Agricultural Soils

2001 ◽  
Vol 9 (2) ◽  
pp. 123-131 ◽  
Author(s):  
M. Confalonieri ◽  
F. Fornasier ◽  
A. Ursino ◽  
F. Boccardi ◽  
B. Pintus ◽  
...  
Keyword(s):  
Total Nitrogen ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Available Phosphorus ◽  
Exchangeable Potassium ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy ◽  
Soil Constituents

The feasibility of near infrared (NIR) reflectance spectroscopy in determining various soil constituents such as total organic carbon, total nitrogen, exchangeable potassium and available phosphorus has been investigated, to monitor their concentration during a long-term agronomic trial. Soil samples previously analysed by conventional chemical methods were scanned using a NIRSystems 5000 monochromator and spectra were treated using several algorithms. The first derivative of each NIR spectrum was used for all statistical analyses. Step-up, stepwise and modified partial least squares (MPLS) regression methods were applied to develop reliable calibration models between the NIR spectral data and the results of wet analyses. MPLS almost always gave the most successful calibrations. The results demonstrated that NIR reflectance spectroscopy can be used to determine accurately two important soil constituents, namely total nitrogen and carbon content. This technique could be employed as a routine testing method in estimating, rapidly and non-destructively, these constituents in soil samples, demonstrating soil variations within a long-term field experiment. For other determinations, such as exchangeable potassium and available phosphorus content, our results were less successful but may be useful for separation of samples into groups.

Quality Control of Technical Casein—Near Infrared Reflectance Spectroscopy Method

1998 ◽  
Vol 6 (A) ◽  
pp. A303-A306 ◽  
Author(s):  
Henryk W. Czarnik-Matusewicz ◽  
Adolf Korniewicz
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Spectroscopy Method ◽  
Total Acidity ◽  
Near Infrared Reflectance ◽  
Significant Saving ◽  
Wet Chemistry ◽  
Large Numbers ◽  
Nir Reflectance Spectroscopy

The near infrared (NIR) reflectance spectroscopy method can be used in the routine checking of the technical casein. All the chemical and physical characteristics of the product that influence the NIR spectrum affect the qualification. In order to monitor possible deviations in the preparation, it is advisable to carry out some test during the different manufacturing stages. These test are: determination of water, fat, ash, free and total acidity. A set of 66 ground casein samples was used to calibrate the output from NIR instrument InfraAlyzer 500 (Bran+Luebbe GmbH), taking reflectance readings every 2 nm between 1100 nm and 2500 nm. As soon as the spectral scanning had been completed, the casein samples were subjected to the standard wet chemistry analysis. The spectral data from this calibration set was then statistically manipulated using MLR method with the aid of the software SESAME ver. 2.10 (Bran+Luebbe GmbH) to generate calibration models. These calibrations were then applied to a separate set of 20 samples which, for validation purposes, were also analysed by wet chemistry. The casein samples analysis predictions compared with the wet chemistry results on these samples, with standard errors of determination of 0.1%, 0.2%, 0.2%, 0.2% and 0.5% for water, fat, ash, free and total acidity, respectively. The use of NIR instrumentation and appropriate calibrations is able to result in a significant saving of laboratory resources when large numbers of the technical casein samples are being processed for analysis.

Visible and near Infrared Reflectance Spectroscopy for the Determination of Moisture, Fat and Protein in Chicken Breast and Thigh Muscle

1996 ◽  
Vol 4 (1) ◽  
pp. 213-223 ◽  
Author(s):  
D. Cozzolino ◽  
I. Murray ◽  
R. Paterson ◽  
J.R. Scaife
Keyword(s):  
Near Infrared ◽  
Correlation Coefficients ◽  
Reflectance Spectroscopy ◽  
Thigh Muscle ◽  
Chicken Breast ◽  
Near Infrared Reflectance ◽  
Multivariate Correlation ◽  
Intact Muscle ◽  
Standard Normal ◽  
Nir Reflectance Spectroscopy

Near infrared (NIR) reflectance spectroscopy was used to determine the chemical composition of chicken breast and thigh muscles. Samples from twenty-four males and twenty-four females were scanned from 400 to 2500 nm, both as intact muscle and as comminuted (minced) tissue. Modified partial least squares (MPLS) regression on scatter corrected spectra (standard normal variates and Detrend) gave calibration models for chemical variables from NIR measurements on the defrosted minced breast samples having multivariate correlation coefficients and standard errors of calibration of 0.995 (2.4), 0.974 (2.11) and 0.946 (4.55) for moisture, crude protein and fat in g kg −1, respectively.

Exploring the Use of near Infrared Reflectance Spectroscopy to Study Physical Properties and Microelements in Soils

2003 ◽  
Vol 11 (2) ◽  
pp. 145-154 ◽  
Author(s):  
A. Moron ◽  
D. Cozzolino
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Agricultural Soils ◽  
Low Cost ◽  
Scatter Correction ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Second Derivative ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy

Near infrared (NIR) reflectance spectroscopy was used to predict the content of silt, sand, clay, iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in soil. A total of 332 samples from agricultural soils (0–15 cm depth) in Uruguay (South America) were used. The samples were scanned in a monochromator instrument (NIRSystems 6500, Silver Spring, MD, USA). Two mathematical treatments (first and second derivative) with SNVD (scatter normal variate and detrend) and without scatter correction were studied. Modified partial least squares (mPLS) was used to develop the calibration models. The coefficient of determination in calibration ( R2cal) and the standard error in calibration ( SEC) using the second derivative were 0.81 ( SEC: 5.1), 0.83 ( SEC: 5.3), 0.92 ( SEC: 2.6) for percent sand, silt and clay, respectively. The R2cal and standard error of cross-validation ( SECV) were for Cu 0.87 ( SEC: 0.7), for Fe 0.92 ( SEC: 21.7), for Mn 0.72 ( SEC: 83.0) and for Zn 0.72 ( SEC: 1.2) on mg kg−1 dry matter. It was concluded that NIR reflectance spectroscopy has a great potential as an analytical method for routine analysis of soil texture, Fe, Zn and Cu due the speed and low cost of analysis.

Modelling of sensory and instrumental texture parameters in processed cheese by near infrared reflectance spectroscopy

2006 ◽  
Vol 73 (1) ◽  
pp. 58-69 ◽  
Author(s):  
Carmen Blazquez ◽  
Gerard Downey ◽  
Donal O'Callaghan ◽  
Vincent Howard ◽  
Conor Delahunty ◽  
...  
Keyword(s):  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Processed Cheese ◽  
Texture Profile ◽  
Texture Parameters ◽  
Nir Reflectance Spectroscopy ◽  
Instrumental Texture

This study investigated the application of near infrared (NIR) reflectance spectroscopy to the measurement of texture (sensory and instrumental) in experimental processed cheese samples. Spectra (750 to 2498 nm) of cheeses were recorded after 2 and 4 weeks storage at 4 °C. Trained assessors evaluated 9 sensory properties, a texture profile analyser (TPA) was used to record 5 instrumental parameters and cheese ‘meltability’ was measured by computer vision. Predictive models for sensory and instrumental texture parameters were developed using partial least squares regression on raw or pre-treated spectral data. Sensory attributes and instrumental texture measurements were modelled with sufficient accuracy to recommend the use of NIR reflectance spectroscopy for routine quality assessment of processed cheese.

Prediction of the Neuro-Toxin Beta-N-Oxalyl-Amino-L-Alanine in Lathyrus Species, Using near Infrared Reflectance Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A93-A96 ◽  
Author(s):  
F. Jaby El-Haramein ◽  
A. Abd-El Moneim ◽  
H. Nakkoul ◽  
P.C. Williams
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Lower Limbs ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Northern India ◽  
Drought Tolerant ◽  
Large Numbers ◽  
Analysis Application ◽  
Nir Reflectance Spectroscopy

Grasspea or chickling vetch ( Lathyrus spp.) is a common food legume, widely grown and eaten in northern India, Bangladesh, Pakistan, Nepal and Ethiopia. It contains the neurotoxin beta-N-oxalyl-amino-L-alanine (BOAA), which can cause the disease known as “neuro-lathyrism”, an irreversible paralysis of the lower limbs, if BOAA-rich seeds form a large proportion of the diet. Lathyrus is a drought-tolerant crop, and ICARDA seeks to breed high-yielding lines that are low in BOAA. Conventional methods for determination of BOAA are time-consuming, expensive, and not practicable for screening large numbers of genotypes. Near infrared (NIR) reflectance spectroscopy offers a rapid, inexpensive method of analysis. Application of NIR reflectance spectroscopy to the prediction of BOAA in Lathyrus has been achieved by developing NIR reflectance spectroscopy equations involving 88 samples, which represented three species: L. sativus, L. cicera and L. ochrus. Both intact and ground seeds were studied. Content of BOAA ranged from 0.09 to 0.83%. Seeds of L. cicera were significantly lower than those of the other two species. The best results were obtained from whole seeds, using multiple linear regression. The standard error of prediction of 0.05% and coefficient of determination ( r2) of 0.94 are considered quite adequate for use in the Lathyrus breeding programme.

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