Why diffuse reflectance can be used in place of Beer's law

"For a smooth surface such as glass, most of the radiation is
reflected from the surface by regular or specular reflection
and no absorption takes place. In the 1100–2500nm
region, the amount of scattering makes the path length
so great that transmittance through 1 cm of most samples
is negligible. This situation is called diffuse reflectance
because most of the incident radiation is reflected. If a
matt surface reflects diffusely without penetration into
the sample, like specular reflectance no absorption takes
place. If, however, some of the radiation penetrates the
surface when it reaches each particle it can be reflected,
absorbed or transmitted. The net result is that the diffusely
reflected radiation (R) can be empirically related to
concentration (c) in an analogous way to Beer’s law
i.e. log 1=R D kc where k is a factor which incorporates
both absorbtivity and path length."

((NEAR-INFRARED SPECTROSCOPY IN FOOD ANALYSIS 1
Near-infrared Spectroscopy
in Food Analysis
Brian G. Osborne
BRI Australia Ltd, North Ryde, Australia))

Why NIR absorbance is lower than IR?

"The reason NIR has an advantage over IR is that NIR has lower absorbance coefficients.
Due to this, the optimum path length without dilution in the IR is in micrometers, where as long
NIR is in millimeters and short NIR is in centimeters. This means that with NIR it is possible to
measure representative sample portions with simple or no sample preparation. It is this lack of
sample preparation makes this method preferred for analysis of agricultural and food materials."

(Near-Infrared Spectroscopy for Analysis of Agricultural Material
Eric C. Newgard)

Detector with high S/N of 50,000 is required for successful NIR

"It is known that the amount of incident light absorbed provides the composition information. If no light is absorbed there is no information available about the sample. On the other hand, if all the light is absorbed there is no information available about the sample. Hence there must be an optimum level of absorbance for acquiring the best information. At high absorbance, the detector noise and path length errors will dominate. Due to the very low band intensities, successful NIR measurements require extremely high signal-to-noise ratios, typically on the order of 50,000 to 1. Typically modern NIR instruments have noise around 10 to 100 micro absorbance units. The theoretical optimum absorbance for detector-noise-limited systems is log (1/R) = 0.434 (T = 37%).
Modern NIR instruments are also capable of adjustment to control the amount of absorbance. Instruments are capable of adjusting the path length, changing the sensing mode, changing the wavelength region, or samples can be diluted with a non-absorber or the particle size can be adjusted. The reason NIR has an advantage over IR is that NIR has lower absorbance coefficients. Due to this, the optimum path length without dilution in the IR is in micrometers, where as long NIR is in millimeters and short NIR is in centimeters. This means that with NIR it is possible to measure representative sample portions with simple or no sample preparation. It is this lack of sample preparation makes this method preferred for analysis of agricultural and food materials. Fig. 5. 90° - 45° Reflectance Configuration"

(Near-Infrared Spectroscopy for Analysis of Agricultural Material
Eric C. Newgard)

Articles on NIR Theory

NIR Theory as published in journal

1. Lin H. and Ying Yibin (2009) Theory and application of near infrared spectroscopy in assessment of fruit quality: a review

Sens. & Instrumen. Food Qual. 3, 130-141.

DOI 10.1007/s 1169-009-9079-z

2. Blanco

3. Nicolai B.M. Beullens, K., Bobelyn et al., (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review

Postharvest Biology and Technology, 46, 99-118,

Doi: 10.1016/j.postharvbio.2007.06.024

4. Aenugu, H.P.R., Kumar, D.S., Srisudharson, Parthiban, N., Ghosh, S.S. and Banji, D. (2011)
    Near Infra Red Spectroscopy- An Overview, International Journal of ChemTech Research, Vol.2, No.2, pp 825-836.

The NIR light was much absorbed and somewhat scattered in peel. The greater the scattering the lower the absorbance.

P.309 The NIR light was much absorbed and somewhat scattered in peel. The greater the scattering the lower the absorbance.

In Kurata and Tsuchikawa, (2009) Application of time of flight near-infrared spectroscopy to fruits:

Applied spectroscopy, Vol. 63, No. 3.

How to determine the thickness of sample where light can penetrate

How to determine the thickness of sample where light can penetrate

In Krivoshiev et al., (2000) A possibility for elimination of the interference from the peel in nondestructive determination of the internal quality of fruit and vegetables by VIS/NIR spectroscopy, Lebensm.-Wiss. U.-Technol., 33, 344-353.

Two samples were excluded because they displayed Mahalanobis H values greater than 4.

In Perez-Marin et al., (2009) Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage. Post Biol and Tech.

P.8 The three classes are clearly distinguished; they are less clearly separated in the graph which yielded the least precise prediction models.

Two samples were excluded because they displayed Mahalanobis H values greater than 4.

Good loading weight interpretation. Variance in loading showed characteristic band.

Good loading weight interpretation.

970nm Sugar negatively correlated.

Variance in loading showed characteristic band.

P.8 Equation loadings were thus plotted (Fig.3) in order to identify, across the spectral range, those points where variance had influenced computing of the model, to a greater or lesser degree, as well as the direction (positive or negative). Fig. 3 shows that for the fruit weight parameter, using the DA-7000, the areas of the spectrum that most influenced the fitting of the model – other than the bands observed in the spectral area which includes the visible region- corresponded to water-absorption bands at around 970 and 1400 nm, and sugar-absorption bands at around 945 and 1300 nm. Moreover, for the first three latent variables – which accounted for 72%, 13% and 6% of variance, respectively= the area around 940 and 970 nm exerted the most influence, with alternating directions: i.e. if it was positive at 940 nm, it was negative at 970 nm and vice versa. This shows the existence of an inverse relationship between variation in water content of the fruit and SSC in the juice, during postharvest storage.

In Perez-Marin et al., (2009) Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage. Post Biol and Tech.

(Scattering) The absorption of radiation increases as fruit firmness decreases i.e. firmer fruit reflect more radiation than softer fruit; this is limited to water and pectin content.

P.6 (Scattering) The absorption of radiation increases as fruit firmness decreases i.e. firmer fruit reflect more radiation than softer fruit; this is limited to water and pectin content.

A key feature of the calibration and validation sets is that it contained data from nectarines sampled at different stages of ripening and storage; this would account for the high coefficient of variation (CV) values recorded, particularly for firmness and fruit weight.

RPD guideline by Williams (2001)

Good loading weight interpretation.

970nm Sugar negatively correlated.

Variance in loading showed characteristic band.

Formula for calculation of Relative interactance = |sample – dark|/|reference - dark|

Formula for calculation of

Relative interactance = |sample – dark|/|reference - dark|

P.168 Kavdir et al., (2007) Visible and near-infrared spectroscopy for nondestructive quality assessment of pickling cucumbers, Post Biol and Tech, 44, 165-174.

Absorption by carbohydrates in the band 800-1050 nm is well known

Absorption by carbohydrates in the band 800-1050 nm is well known (Williams and Norris, 1987). The by-products of cell wall degradation absorb above 900 nm and induce extra absorption in this band (Wilson et al., 2000).

P. 419 in Fan et al., (2009) Determination of soluble solids and firmness of apples by Vis/NIR transmittance, J. of Food Eng., 93, 416-420.

A persistent spiky sequence in a B-vector regression coefficient display normally indicates over-fitting of the data

The model, as represented by B-vector of regression coefficients, showed strong oscillatory behaviour across the spectral range with numerous spikes above 950 nm. A persistent spiky sequence in a B-vector regression coefficient display normally indicates over-fitting of the data. This supports the contention that  the PLS models are using too many latent variables. The largest regression coefficient occurred at 914 nm. This is consistent with the presence of the third overtone of the carbohydrate CH absorbance band that nominally occurs in the 900-920 nm range (Williams and Norris, 1981). This absorbance band for DM and/or sugar determination as it is removed from troublesome interferences from the water absorbance bands that typically dominate spectra of fruit.

P. 303-304 in Clark et al., (2003) Dry matter determination in Hass avocado by NIR spectroscopy, Post Biol and Tech, 29, 300-307 

Good interpretation of regression coefficients and chemical band assignment

Good interpretation of regression coefficients and chemical band assignment

In Clark et al., (2003) Dry matter determination in Hass avocado by NIR spectroscopy, Post Biol and Tech, 29, 300-307.

NIRS prediction of total acidity has been difficult to achieve, due to the relatively low levels of organic acids in fruit.

Organic acids contribute to the SSC to an extent of 10% in citrus fruit, yet NIRS prediction of total acidity has been difficult to achieve, due to the relatively low levels of organic acids in fruit.

P.75 in Cayuela (2008) Vis/NIR soluble solids prediction in intact oranges cv. Valencia late by reflectance, Post Biol and Tech, 47, 75-80.

Reference spectrum (empty bottle)

Reference spectrum (empty bottle), Mahalanobis distance, %T USB200 Ocean optics

P. E126 in Yu et al., (2007) Vintage year determination of bottled Chinese rice wine by Vis-NIR spectroscopy, J. of Food Science, Vol.72(3)

A good model should have a low SEC, a low SECV and a high correlation coefficient but also a small different between SEC and SECV.

A good model should have a low SEC, a low SECV and a high correlation coefficient but also a small different between SEC and SECV.

P.318

Standard error of cross-validation (SECV or SEP) P.318

A large difference indicates that too many latent variables are used in the model and noise is modeled. P.318

In Li et al., (2007) Nondestructive measurement and fingerprint analysis of soluble solid content of tea soft drink based on Vis/NIR spectroscopy, J. of Food Eng, 82, 316-323.

The regression coefficients was validated to be an effective way for the selection of effective wavelengths.

The regression coefficients was validated to be an effective way for the selection of effective wavelengths.

P.16 in Liu et al., (2008) Determination of effective wavelengths of discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis, Analytica chimica acta, 615, 10-17

The wavelengths important in classifying the species of interest were determined based on PLS regression coefficients and differences in spectra

The wavelengths important in classifying the species of interest were determined based on PLS regression coefficients and differences in spectra.

P.761 in Jia et al., (2007) Differentiating tobacco budworm and corn earworm using near-infrared spectroscopy, J. Econ.Entomol. 100(3). 759-764.

Example of regression coefficient interpretation

"How to interpret regression coefficient for sensitive wavelength
P.1018 in Shao and He (2007) Nondestructive measurement of the internal quality of bayberry juice using Vis/NIR spectroscopy, J of Food Eng 79, 1015-1019."

Good interpretation of Coomans plot

"Page 95 of following paper gives good discussion on Coomans plot"

(Mouzzen et al., 2005. Near infrared spectroscopy for agricultural materials: an instrument comparison, J. Near Infrared Spectrosc. 13, 87-97.)

Good discussion to give reason for less accuracy in terms of less no. of samples but greater variation

"Page 93: However, the results were not as good as previously reported, when samples were all taken from one field including three textures only. This was considered due to the smaller number of samples used, involving greater variation of soil texture and colours."

(Mouzzen et al., 2005. Near infrared spectroscopy for agricultural materials: an instrument comparison, J. Near Infrared Spectrosc. 13, 87-97.)

Coomans plot good description (SIMCA)

"Page 91 of following paper gives good description for SIMCA involving Coomans plot"

(Mouzzen et al., 2005. Near infrared spectroscopy for agricultural materials: an instrument comparison, J. Near Infrared Spectrosc. 13, 87-97.)

Criterion for accuracy parameters of calibration model: R2, RPD etc.

"Page 90 in Mouazen et al., gives details of criterion of parameters used to evaluate accuracy of calibration model such as r2, RPD etc."

(Mouzzen et al., 2005. Near infrared spectroscopy for agricultural materials: an instrument comparison, J. Near Infrared Spectrosc. 13, 87-97.)

Range/SEP is one of validation values

"R=Range

SEP = Standard Error of Prediction as performed on an independent test set

Guidelines R/SEP

≥   4 = screening calibration

≥ 10 = acceptable for calibration for quality control

≥ 15 = good calibration for quantification

In other words, be careful when presented calibrations with good SEP. If you don’t have the range, you cannot evaluate the SEP!

High range typically gives higher SEP"

(From Power Point slide of togni)

The log(1/R) is valid in case of scattering despite unknown path length t in Beer's law

"For a smooth surface such as glass, most of the radiation is
reflected from the surface by regular or specular reflection
and no absorption takes place. In the 1100–2500nm
region, the amount of scattering makes the path length
so great that transmittance through 1 cm of most samples
is negligible. This situation is called diffuse reflectance
because most of the incident radiation is reflected. If a
matt surface reflects diffusely without penetration into
the sample, like specular reflectance no absorption takes
place. If, however, some of the radiation penetrates the
surface when it reaches each particle it can be reflected,
absorbed or transmitted. The net result is that the diffusely
reflected radiation (R) can be empirically related to
concentration (c) in an analogous way to Beer’s law
i.e. log 1=R D kc where k is a factor which incorporates
both absorbtivity and path length."

((NEAR-INFRARED SPECTROSCOPY IN FOOD ANALYSIS 1
Near-infrared Spectroscopy
in Food Analysis
Brian G. Osborne
BRI Australia Ltd, North Ryde, Australia))

Advantages and disadvantages of FT-NIR

"Advantages and disadvantages of Fourier Transform spectrometric techniques compared to traditional analytical methods
In a dispersive spectrometer, wavenumbers are observed sequentially. In an FT-IR and FT-NIR spectrometer, all the wavenumbers of light are observed simultaneously. Therefore, when spectra are collected under identical conditions the signal-to-noise (S/N) ratio of the FT-IR spectrum will be greater than that of the dispersive IR spectrum (HILL et al. 1997).

In FT-IR instruments there is no need to limit the beam width in order to obtain an adequate resolution. In fact, a circular optical aperture is used in FT instruments, and the beam area is 75 to 100 times larger than the slit area of dispersive instruments (SETTLE 1997). As a consequence, there is an advantage of increased beam intensity going through the sample and therefore a much higher throughput with a FT-IR than with a dispersive instrument (JACQUINOT 1984).

High and constant resolution: Spectral resolution is a measure of how well a spectrometer can distinguish closely spaced spectral features. Filter instruments cannot offer high resolution because, in dispersive instruments, resolution decreases as lower frequencies are scanned. In FT-IR, the resolution depends on the optical path difference (OPD) that can be achieved. Thus it is constant across the scanning range (WILKS 1986, PERKINS 1987)."

(Near Infrared spectroscopy, a quality control tool for the different steps in the manufacture of
herbal medicinal products
Magali Laasonen nÈe Grata
ACADEMIC DISSERTATION
Division of Pharmacognosy
Department of Pharmacy
Faculty of Science
University of Helsinki
HELSINKI 2003)

"Two important parameters to ensure good spectra collection of a FT-NIR spectrometer during an analysis are number of scans and resolution. Resolution in an FT-NIR determines a small frequency interval that can be distinguished over a spectral range and typically this parameter ranges from 4 and 64cm-1. In case of selecting a high resolution, the spectrum becomes more detailed, but it captures more noise and the analysis takes a longer time. The number of scans acquired enhances the signal amplitude per unit time. This parameter is inversely proportional to noise effect, and the typical values of number of scans are between 16 and 128 (PLSplus IQ user’s guide; Thermo Electron Corporation, Salem, NH, USA). Setting these parameters is based on a compromise between the operation time and analysis quality desired."
(FT-NIR spectroscopy and Laser Diffraction particle sizing of
APIs in Pharmaceutical formulations, Joana Lúcia Carrilho Figueiredo
Dissertação para obtenção do Grau de Mestre em
Mestrado Integrado em Engenharia Química, Setembro de 2008)

"Each spectrum had an average of 64 scans and provided a resolution
of 16cm-1. The spectral data analysis covered the range from 3996.2 to 12004 cm-1
. "

Detectivity comparison between Si and InGaAs detectors

(http://bwtek.com/spectrometer-part-3a-the-detector/)

NIR wavelength range suitable for infestation prediction

"The  950-1690nm bands were idenfified as important for infestation prediction. In general, NIR spectroscopy should be a feasible technique for rapid classification of insect infestation in fruit"

(Comparison of three near infrared spectrophotometers for infestation detection in wild blueberries using multivariate calibration models
B.N. Peshlov et al., J. Near Infrared Spectrosc. 17, 203-212 (2009))

Comparison of spectrophotometer in NIRS for agricultural materials

"The selection of a spectrophotometer for the measurement of constituents of agricultural materials with acceptable accuracy and cost effectiveness requires a comparative study of the performance of different spectrophotometers. Four commercially available spectrophotometers were evaluated, based on measurements performed on three agricultural materials. These spectrophotometers, differing mainly in wavelength range and measurement principles, comprised a diode array (DA) of 300-1700 nm, a combination of diode array and scanning monochromator (DASM) of 350-2500 nm, a Fourier transform (FT) of 750-2500 nm and a scanning monochromator (SM) of 400-2500 nm spectrophotometers. They were used to measure the moisture content of soil, the chemical constituents of hog manure and to detect bruising in apples. Three spectral pre-treatments were considered. Calibrations were developed using partial least squares (PLS) regression with the leave-one-out cross-validation technique for soil and manure and principal component analysis (PCA) for apple. The four instruments provided good predictions for soil moisture content, with the largest coefficient of determination (r(2)) values between 0.84-0.86 and with the largest ratio of prediction to deviation (RPD) of standard deviation (SD) to root mean square error of cross-validation (RMSECV) ranging from 2.53 to 2.75. The DASM and SM were comparable and slightly better than the DA and FT. For hog manure, total nitrogen was predicted more accurately with the four instruments (r(2) = 0.83-0.89 and RPD = 2.43-3.01) than the phosphorus (r(2) = 0.66-0.85 and RPD = 1.72-2.61) and potassium (r(2) = 0.70-0.84 and RPD = 1.83-2.50). The order of accuracy of the four spectrophotometers for the measurement of total nitrogen was: FT-SM-DA-DASM. For phosphorus and potassium the order was: DA-DASM-FT-SM and SM-FT-DASM-DA, respectively. The DA performed better than the DASM and FT for discrimination of bi-colour and single-colour bruised apple, respectively. Therefore, selection of a spectrophotometer depends mainly on the type of material analysed and the constituent to be measured. Wavelengths above 1700 nm were found unnecessary for the applications considered and the DA spectrophotometer was of sufficient accuracy, as it is robust, significantly cheaper and can be used in the field for on-line measurements."

(Near infrared spectroscopy for agricultural materials: an instrument comparison
Mouazen, Abdul Mounem ×
Saeys, Wouter
Xing, Juan
De Baerdemaeker, Josse
Ramon, Herman
Journal of near infrared spectroscopy vol:13 issue:2 pages:87-97)

Smooth and matt surface affects specular reflection

"For a smooth surface such as glass, most of the radiation is
reflected from the surface by regular or specular reflection
and no absorption takes place. In the 1100–2500nm
region, the amount of scattering makes the path length
so great that transmittance through 1 cm of most samples
is negligible. This situation is called diffuse reflectance
because most of the incident radiation is reflected. If a
matt surface reflects diffusely without penetration into
the sample, like specular reflectance no absorption takes
place. If, however, some of the radiation penetrates the
surface when it reaches each particle it can be reflected,
absorbed or transmitted. The net result is that the diffusely
reflected radiation (R) can be empirically related to
concentration (c) in an analogous way to Beer’s law
i.e. log 1=R D kc where k is a factor which incorporates
both absorbtivity and path length."

((NEAR-INFRARED SPECTROSCOPY IN FOOD ANALYSIS 1
Near-infrared Spectroscopy
in Food Analysis
Brian G. Osborne
BRI Australia Ltd, North Ryde, Australia))

Limit of Beer's law for variation in path length in sample

"Radiation interacting with a sample may be absorbed,
transmitted or reflected. In the classical spectroscopy
experiment, reflection is eliminated so that the proportion
of radiation attenuated by the sample may be measured as
transmittance. Beer’s law then defines a proportionality
between transmittance and the product of concentration
of the absorbing species and path length. For a clear
transparent liquid sample such as beer, hot starch melts,
wine or vegetable oil, the path length may be fixed by
means of a static or flow-through sample cuvette or a pair
of fiber-optic probes and a calibration developed using
samples of known concentrations. For example, Halsey.2/
used standard solutions of ethanol in water to develop
a calibration for alcohol content of beer. It should be
noted that, owing to the relatively weak intensities of
NIR absorption bands, samples such as vegetable oils
may be analyzed without dilution in a solvent.
Beer’s law is only valid in the absence of light scatter
in the sample. Scattering changes the path length through
which the radiation passes and, because the amount
of scattering varies from sample to sample, the path
length cannot be defined. This type of experiment is
known as diffuse transmittance (Figure 3), the most well known
example of which is liquid whole milk. The
fat globules in the milk scatter light in the manner
shown and invalidate Beer’s law. Diffuse transmittance
measurements are usually carried out in the 800–1100nm
region of the spectrum where the weak absorptions enable
useful data to be obtained using thicknesses of 1–2 cm
of samples such as meat, cheese or whole grain. Near infrared
transmittance (NIT) instruments are particularly
applicable to the analysis of whole grains and a typical
apparatus is shown schematically in Figure 4. A sample
of grain is placed in a hopper from where aliquots are
dispensed into the measurement chamber. When analysis
is complete, the sample is discharged into a collection
tray."

(NEAR-INFRARED SPECTROSCOPY IN FOOD ANALYSIS 1
Near-infrared Spectroscopy
in Food Analysis
Brian G. Osborne
BRI Australia Ltd, North Ryde, Australia)

The ideal training set

"All the chemometric calibration methods rely on correlation between some derived spectral measurements and reference measurements for the samples in what, for obvious reasons, is called a training set. It is crucial to the future robustness of the calibration that this training set is representative of the unknowns for which predictions are to be made. ..... The chemometric method has to learn, from the training set, how to make predictions that are robust to variations in the spectra caused by physical properties of the sample and by constituents other than the one of interest. If these sources of variability are not present in the training samples, the resulting calibration will not be robust against them. For example, if all the training samples in an exercise to calibrate for wheat protein are scanned at 14% moisture content, because they have all equilibrated in a laboratory whilst waiting to be scanned, then there is every chance that the calibration will not work well for samples scanned at other moisture contents. There are two solutions to this: equilibrate all the unknowns in the same way, or ensure that the moisture variations in the training set are representative of those that will occur in the unknowns.
   .......The ideal training set is a random sample taken from all the unknowns that the calibration will ever be used on. In most cases this is an unachievable ideal, but we should at least try to represent the main sources of variability in the samples. Key ones are particle size, when powders or ground samples are being measured, and moisture content. For example, if the samples come from an industrial process, it is important to include samples from several batches."

(NIR Celebration Special Issue of NIR news)

Standard material should have high and fairly constant absolute reflectance

"The standard is
usually a stable material with a high and fairly constant absolute
reflectance (e.g., Teflon, barium sulfate, magnesium oxide,
high-purity alumina ceramics). Standard is a material that absorbs no light at any wavelength and
reflects light at an angle identical with the incidence angle."

(Critical Review
Near-infrared spectroscopy in the pharmaceutical
industry
M. Blanco*, J. Coello, H. Iturriaga, S. Maspoch and C. de la Pezuela
Analyst, August 1998, Vol. 123 (135R–150R))

Relative reflectance is preferred to absolute reflectance

"The Kubelka–Munk function, f(R°), is given by

where R° is the absolute reflectance of the sample (viz., the
fraction of light impinging on it that is reflected), k its
absorption coefficient and s its dispersion coefficient. In
practice, relative reflectance (R), which is the ratio of the
intensity of the light reflected by the sample to that by a
standard, is preferred to absolute reflectance. The standard is
usually a stable material with a high and fairly constant absolute
reflectance (e.g., Teflon, barium sulfate, magnesium oxide,
high-purity alumina ceramics)."

(Critical Review
Near-infrared spectroscopy in the pharmaceutical
industry
M. Blanco*, J. Coello, H. Iturriaga, S. Maspoch and C. de la Pezuela
Analyst, August 1998, Vol. 123 (135R–150R))

Why detector position must be set to avoid regular reflectance

"Reflectance spectroscopy measures the light reflected by the
sample surface, which contains a specular component and a
diffuse component. Specular reflectance, described by Fresnel’s
law, contains little information about composition; consequently,
its contribution to measurements is minimized by
adjusting the detector’s position relative to the sample. On the
other hand, diffuse reflectance, which is described by the
Kubelka–Munk theory,36 is the basis for measurements by this
technique."

(Critical Review
Near-infrared spectroscopy in the pharmaceutical
industry
M. Blanco*, J. Coello, H. Iturriaga, S. Maspoch and C. de la Pezuela
Analyst, August 1998, Vol. 123 (135R–150R))

Low molar absorptivity at low wavelength lead to low absorbance in NIR.

"The low molar absorptivity of adsorption bands in the NIR
region (typically between 0.01 and 0.1 l mol–1 cm–1) severely
restricts sensitivity; however, it permits operation in the
reflectance mode and hence the recording of spectra for solid
samples."

(Critical Review
Near-infrared spectroscopy in the pharmaceutical
industry
M. Blanco*, J. Coello, H. Iturriaga, S. Maspoch and C. de la Pezuela
Analyst, August 1998, Vol. 123 (135R–150R))

Figure of bottles with different particle size presenting scattering effect

Internal and external validation

"In order to assess the accuracy of the calibration model and
to avoid overfitting, validation procedures have to be applied; a
calibration model without validation is nonsense. Leverage correction
is an equation based procedure to estimate the prediction
accuracy without performing any prediction, and is to be avoided
at all times because it always leads to overoptimistic estimates.
In leave-one-out cross validation, one sample is removed from
the dataset, and a calibration model is constructed for the remaining
subset. The removed samples are then used to calculate the
prediction residual. The process is repeated with other subsets
until every sample has been left out once, and in the end the
variance of all prediction residuals is estimated. In multifold
cross validation, a well-defined number of samples (‘segment’)
are left out instead of one. In internal validation, the dataset
is split into a calibration and a validation set. The calibration
model is constructed using the calibration set, and the prediction
residuals are then calculated by applying the calibration
model to the validation set. In external validation, the validation
dataset is independent, and is, for example, obtained from
a different orchard or different season. Although leverage correction
should not be used, it is still the default cross validation
method in the widely used Unscrambler chemometrics software
(http://www.camo.com)."

(Postharvest Biology and Technology 46 (2007) 99–118
Review
Nondestructive measurement of fruit and vegetable quality
by means of NIR spectroscopy: A review
Bart M. Nicolai ,, Katrien Beullens, Els Bobelyn, Ann Peirs,
Wouter Saeys, Karen I. Theron, Jeroen Lammertyn)

Advantage and disadvantage of AOTF

"In AOTF (Acoustic Optical Tuneable Filter)
wavelength selectors light is directed into a crystal of
TeO2 ( figure 6). A high-frequency acoustic wave in
the radio frequency range is coupled into the crystal by
the use of a piezoelectric material bonded to the
crystal. These acoustic waves quickly propagate
through the crystal, interact with the broadband light
and generate two monochromatic beams of light, each
polarized in a different direction. These
monochromatic beams are coupled by using optical
fibres and can be used as a source of NIR light and
sent to the sample. Advantages of AOTF are, it has no
moving parts, adjustable intensity and gives narrow
beams. The disadvantages include, it covers spectrum
at limited wavelength range (1000-2000nm) and
difficulties when measuring highly absorbing samples."

(International Journal of ChemTech Research
CODEN( USA): IJCRGG ISSN : 0974-4290
Vol. 3, No.2, pp 825-836, April-June 2011
Near Infra Red Spectroscopy- An Overview
Hari Prasad Reddy Aenugu, D.Sathis Kumar*, Srisudharson,
N. Parthiban, Som Subhra Ghosh, David Banji)

InGaAs is better than PbS

(International Journal of ChemTech Research
CODEN( USA): IJCRGG ISSN : 0974-4290
Vol. 3, No.2, pp 825-836, April-June 2011
Near Infra Red Spectroscopy- An Overview
Hari Prasad Reddy Aenugu, D.Sathis Kumar*, Srisudharson,
N. Parthiban, Som Subhra Ghosh, David Banji)

Comparison between NIR materials for detectors

"The choice of detectors depends on Wavelength range,
Spectrometer design characterstics, detector
characteristics such as photosensitivity (responsivity),
noise equivalent power (NEP), detectivity.
Photosensitivity measures the voltage output per unit
of incident radiant at a particular wavelength when
noise is not considerable. NEP measures the quantity
of light when the signal to noise ratio is 1. Detectivity
is a parameter used to compare the performance of
different detectors. Best detector should possess the
higher in the signal of detectivity. Detectity is the
signal to noise ratio at particular electrical frequency
and in a 1HZ bandwidth when 1 watt of radiant on a
1cm2 active area detector. Detectors (table 1) using in
NIR spectrometers are Lead sulphide detectos (PbS),
Lead selenide detectors (PbSe), Silicon detectors,
Indium antimonide detectors, InGaAs, InSb, Common
Charged Coupled Devices (CCD)."

(International Journal of ChemTech Research
CODEN( USA): IJCRGG ISSN : 0974-4290
Vol. 3, No.2, pp 825-836, April-June 2011
Near Infra Red Spectroscopy- An Overview
Hari Prasad Reddy Aenugu, D.Sathis Kumar*, Srisudharson,
N. Parthiban, Som Subhra Ghosh, David Banji)

Article containing table of NIR band assignment

"In this technique, some groups absorb
characteristically within a definite range. The shift in
position of absorption for a particular group may
change with the changes in the structure of molecule.
The force constant is responsible for the absorption
peaks can be used to calculate bond distances and bond
angle in simple cases. When the near infrared spectrum
of unknown compound is scanned numbers of
questions come to our mind such as which groups are
present in the compound, what environments are
influencing it or what type of carbon skeleton is
present in the compound. The characteristic groups
absorb light in definite frequency. So this technique is
quite useful to predict the presence of functional
groups and to identify the compounds. Figure 9 and
table 2 express the characterization of group
absorption region in NIR."

(Near Infra Red Spectroscopy- An Overview
Hari Prasad Reddy Aenugu, D.Sathis Kumar, Srisudharson,
N. Parthiban, Som Subhra Ghosh, David Banji
International Journal of ChemTech Research
CODEN( USA): IJCRGG ISSN : 0974-4290
Vol. 3, No.2, pp 825-836, April-June 2011)

Good Figure of how overlapping peaks constitute broad NIR bands

Good Figure of how overlapping peaks constitute broad NIR bands
(Osborne, 2007)

External diffuse reflection

Reflection is due to three different phenomena. Specular
reflection causes gloss, whereas external diffuse reflection is
induced by rough surfaces. Both only provide information about
the surface of the sample. Scattering results from multiple refractions
at phase changes inside the material. The main scattering
elements in fruit and vegetables are the cell wall interfaces since
they induce abrupt changes in refractive index (McGlone et al.,
1997), but suspended particles, such as starch granules, chloroplasts
and mitochondria may also induce scattering caused by
diffraction at the particle surface where the refractive index is
different from that of the surroundings (Il’yasov and Krasnikov,
1991). The scattering is also dependent on the size, the shape
and microstructure of the particles. Scattering may also appear
due to heterogeneities, such as pores, openings, capillaries that
are randomly distributed through the sample. Multiple scattering
events largely determine the intensity of the scattered
light that is emitted (McGlone et al., 1997). The scattering process
affects the intensity level of the reflected spectrum rather
than the shape; the latter is more related to the absorption
process.

(Nicolai et al., (2007) Postharvest Biology and Technology 46, 99–118)

Diffuse transmittance

Diffuse Transmittance

Radiation interacting with a sample may be absorbed,
transmitted or reflected. In the classical spectroscopy
experiment, reflection is eliminated so that the proportion
of radiation attenuated by the sample may be measured as
transmittance. Beer’s law then defines a proportionality
between transmittance and the product of concentration
of the absorbing species and path length. For a clear
transparent liquid sample such as beer, hot starch melts,
wine or vegetable oil, the path length may be fixed by
means of a static or flow-through sample cuvette or a pair
of fiber-optic probes and a calibration developed using
samples of known concentrations. For example, Halsey.2/
used standard solutions of ethanol in water to develop
a calibration for alcohol content of beer. It should be
noted that, owing to the relatively weak intensities of
NIR absorption bands, samples such as vegetable oils
may be analyzed without dilution in a solvent.
Beer’s law is only valid in the absence of light scatter
in the sample. Scattering changes the path length through
which the radiation passes and, because the amount
of scattering varies from sample to sample, the path
length cannot be defined. This type of experiment is
known as diffuse transmittance (Figure 3), the most well known
example of which is liquid whole milk. The
fat globules in the milk scatter light in the manner
shown and invalidate Beer’s law. Diffuse transmittance
measurements are usually carried out in the 800–1100nm
region of the spectrum where the weak absorptions enable
useful data to be obtained using thicknesses of 1–2 cm
of samples such as meat, cheese or whole grain. Near infrared
transmittance (NIT) instruments are particularly
applicable to the analysis of whole grains and a typical
apparatus is shown schematically in Figure 4. A sample
of grain is placed in a hopper from where aliquots are
dispensed into the measurement chamber. When analysis
is complete, the sample is discharged into a collection
tray. This arrangement lends itself well to adaptation for
on-line measurements (see section 5.1).

(Osborne NEAR-INFRARED SPECTROSCOPY IN FOOD ANALYSIS
Encyclopedia of Analytical Chemistry
Edited by Robert A. Meyers. Ó John Wiley & Sons Ltd, Chichester. ISBN 0471 97670 9)

Definition of Interactance

Definition of Interactance

Three different measurement setups for obtaining near
infrared spectra are shown in Fig. 2. In reflectance mode
(Fig. 2a), light source and detector are mounted under a specific
angle, e.g., 45◦, to avoid specular reflection. In transmittance
mode the light source is positioned opposite to the detector,
while in interactance mode the light source and detector are
positioned parallel to each other in such a way that light due
to specular reflection cannot directly enter the detector. This
can be achieved by means of a bifurcated cable in which fibers
leading to the source and detector are parallel to each other and
in contact with the product, or by means of a special optical
arrangement (e.g., Greensill and Walsh, 2000a; McGlone et al.,

(Nicolai et al., 2007)

There is also similar definition of interactance by Karl Norris somewhere. May be in Davies Column but I cannot find it.

In the interactance mode
(Figure 6d) a higher probability is given to the incident
beam to interact with the sample. Consequently, the
emerging beam (collected at a place somewhat distant from
the location of incidence) contains more information on
the sample constituents and reflects better the actual
composition of the sample.

(Pasquini, 2003)