Quality Measurements Using NIR/MIR Spectroscopy: A Rotten Apple Could Turn Your Product into a Lemon

A rotten apple could turn your product into a Lemon

NIR/MIR Spectroscopy

Focusing on Inline Use

The Spectrum

Region Wavelength Wavenumbers (cm-1) Frequencies (Hz)

Near UV 200 to 350 nm

Vis 400 to 700 nm Xx to 12800

NIR 700 to 2500 nm 12800 to 4000 3.8x1014 to 1.2x1014

Mid IR 2.5 to 50 um 4000 to 200

Far IR 50 to 100 um 200 to 10

How light is used

How light is used

Glucose Water Spectra

Regions

• Near UV – electronic transitions of the energetic levels of valence orbitals, absorption of peptidic bonds in proteins, and aromatic amino acids.

• Vis – electronic transitions occur in molecules with large numbers of conjugated double bonds. i.e. carotenoids, chlorophylls, and porphyrins.

Regions cont…..

• NIR – first spectral region exhibiting absorption bands related to molecule vibrations, widely used for composition analysis of food products.

• MIR – main region of vibrational spectroscopy. This region retains information allowing organic molecules such as proteins, polysaccharides, and lipids to be characterized.

Molecular spectroscopy

• Analysis and quantification of molecular responses to introduced radiation.

• Energy exchange occurs between the radiation energy and the energy contained within the molecule.

Molecular spectroscopy…………….

See Wiki for live example

Molecular Groups

• Sensitive to molecules containing C-H, O-H, and NH bonds.

• Interact with NIR portion of spectrum• Starch and sugars (C-H)• Alcohols, moisture and acids (O-H)• Protein (N-H)

MIR versus NIR

• MIR absorption fundamental vibration energies in the mid-IR part of the electromagnetic spectrum.

• NIR absorption overtones 1st, 2nd, and 3rd and combinations of CH, NH, and OH vibrations occur in the near-IR part of the electromagnetic spectrum.

NIR overtones and combinations

NIR region

Beers Law = € ℓ c

BEER-LAMBERT, LAMBERT-BEER, BEER-LAMBERT-BOUQUER.

Beers Law …………

Absorbance = € ℓ c

€ = molar absorptivity, ℓ = path length, and c = concentration.

The Beer’s not perfect

Deviates because• Particle scatter• Interferents, minute contaminants• Molecular interactions• Changes in refractive index• Stray light• Changes in sample size/path length.

Better Beer

Corrected by (data preprocessing)• MSC (multiplicative scatter correction)• SNV (standard normal variate correction) and

normalization• Baseline correction• Differentiation (Savitzky-Golay)

Collection of Spectra

Diffuse Reflection

Interactance

Beers Law …………again

Absorbance = log10 ( Iref/I )= € ℓ c

Iref = intensity of source light

I = intensity of light through sample

€ = molar absorptivity, ℓ = path length, and c = concentration.

How to measure

Wavelength Selection

Filters• Optical interference

(rotating disk with 9 x filter)• Optical Tunable Filters (AOTF)• Liquid crystal tunable filters (LCTF)

Wavelength Selection………..

Monochromator• Disperses light with a wide range of

wavelengths into monochromatic light at a different wavelength. (Rotating diffraction grating or interferometer).

• Classified as pre and post dispersive

Wavelength Selection………..Diffraction Grating

NIR region detectors

Material Range (cm-1) Wavelength Unit Wavelength UnitSilicon 16700-

9000599 nm 1111 nm

InGaAs (Indium Gallium Arsenide)

12000-6000

833 nm 1667 nm

PbSe (Lead Selenide) 11000-2000

909 nm 5000 nm

MCT (Mercury Cadmium Telluride)

117000-400

85 nm 25000 nm

DTGS (Deuterated triglycine sulfate)

12000-350 833 nm 28571 nm

MIR region detectors

Material Range (cm-1) Wavelength Unit Wavelength Unit

DTGS / KBr 12000-350 833 nm 29 umDTGS / Csl 6400-200 1563 nm 50 um

MCT 11700-400 855 nm 25 umPhotoacoustic 10000-400 1000 nm 25 um

DTGS – thermo capacitive device, inexpensive but have low sensitivity and slow in response.MCT – semiconductor where IR radiation causes changes in electron conduction. Faster and more sensitive (higher SNR) than DTGS, but need to be cooled and have narrow bandwidth.KBr – Potassium Bromide, Csl

Transmission of Light

Transmission of Light……………….

Dispersive Spectrometer

Grating-based dispersive spectrometer• Low Cost ($1500 to $15k)• Fast spectrum acquisition (<10msec)• Silicone or InGaAs ( 300nm to 2.5um)• External trigger for inline use• Miniature models available• Hand Held units available• Electronic cooling down to -10 deg C.

Dispersive Spectrometer

FT-NIRPreferred method for food composition and

quality, why –• Quick spectrum acquisition (< 0.5 sec)• User friendly easy to use Chemometrics

packages• Inline usable via fiber optics• Stable and repeatable results• Superior sensitivity

FT-NIR…………….

FT-NIR…………….

FT compared to Dispersive

MIR pros/consMIR advantages are –• Part of the spectrum that contains fundamental vibrations• Well defined bands for organic functional groups• Good for qualitative and quantitative identification of

functional groupsMIR disadvantages -• Available energy drops off rapidly with increasing

wavelength• Expensive transmitting materials• High absorption means path lengths have to be small.• Sample preparation required

NIR prosNIR advantages are –• Cheap transmission with glass optics• Instruments simpler and cheaper to manufacture.• Non destructive, no sample preparation required because NIR

bands 10-100 times less intense.• Good for qualitative and quantitative identification via

combination bands and overtones of functional groups.• Weak absorption due to water overtones enables analysis of

high moisture products.• Lower absorption means longer path lengths. 1 to 10mm.• Extremely high signal-to-noise in spectral data enables

Chemometrics to extract compositional information.• Not influenced by CO2 eliminating instrument purging.

NIR consNIR disadvantages are -• Less information contained in spectra.• Combination and overtone bands make

association with individual chemical groups more difficult.

• Generally can’t indentify components of less than 1% in product.

• Need more robust calibration techniques.• Relies on Chemometrics – PCA, PLS, SIMCA• Robustness of calibrations needs to be monitored.

NIR example

Diffused Reflectance absorbance raw, and 2nd Derivative of Bacillus cereus.

MIR example

System Overview

ChemometricsThe practice of applying mathematical tools in

order to extract chemical or physical information from a dataset (NIR spectra).

Normally involves the following steps –• Data preprocessing (base line removal,

filtering, scatter correction)• Data reduction and visualisation. (PCA, SIMCA)• Outlier detection• Qualitative and Quantitative model

development. (PCA-R, PLS)

Model Development

Model Development

Unsupervised –• sample clusters in a multidimensional space

created by a Principle Components Regression (PCA-R)

• resulting model will predict group classification

• Samples which do not belong to a group can be classified as outliers

PCA-R

Austria( ), Switzerland(□), Germany( ), France Thermized(■ ▲ x), France Raw(o), and Finland(• ).

Model Development…………

Supervised –• means we have allocated a result for each

sample and a Partial Least Squares (PLS) regression generates a model to predict the result in future samples.

• Samples which do not belong to the model can be classified as outliers.

System Overview

Models in Prediction

Water Soluble Nitrogen (WSN) validation of FT-MIR recorded on European Emmental Cheeses produced during summer.

Non protein nitrogen (NPN) validation of FT-MIR recorded on European Emmental Cheeses produced during summer.

Data Reduction via PCA

For a photo diode array there will be 255 variables for each spectra. This is normally reduced down to less than 10 using PCA.

After mean centering.

PCA example

Defining our Results

RMSEP (Root Mean Square Error of Prediction)• RMSEP = SQRT(∑(ai-pi-bias)2/n-1)

where ai=actual value and pi=predicted value.

RMSECV (Root Mean Square Error of Cross Validation)

• Calculated as per RMSEP• Predicted results are determined for samples not

included in the initial calibration model. • Best indication of how well your model is doing.

Defining our Results…………………

R or R2 (coefficient of determination)• Quantifies how well the predicted-v-actual

values fit onto a straight line.

RPD• RPD = SD/SECV where SD=standard deviation.• RPD best if >3 for the model to be reliable.

NIR spectroscopy Advantages• Minimal to no sample preparation• Deeper sample penetration than Mid Nir• Able to measure many constituents simultaneously• High Scan Speed ( < 1sec)• High Resolution ( Grating – 0.2cm-1, FT – 0.1 to 0.005-1 )• Wide range of application ( almost all organic and some

inorganic )• Quantitative and Qualitative results• No phase constraints – gas, liquid or solid.• Non Destructive, non contact.• Faster, safer working environment that does not require

chemicals

NIR spectroscopy Disadvantages

• Some insight required in sample selection for model development.

• Black Box – not able to easily understand how results are determined.

• Model development and maintenance is an ongoing expense.

• Typically able to measure organic constituents above 1 % (approx)

Examples - Meat (Beef)• Packaged Beef - Fat, protein, water content in emulsified

meats. (1300-2000nm, R>0.9)• Online – Lean beef blended to increase %fat. Five wavelengths

to measure fat and water then calculated protein. (Wavelengths 1441, 1510, 1655, 1728, 1810nm). SEP_fat(1.5% for 7 to 26%), SEP_water(1.3% for 58 to 78%), and SEP_protein(0.7% for 15 to 21%).

• Intramuscular fat – RMSEP(1.2% for 1 to 14%) using 1100 -2500nm and R2>0.98.

• Tenderness – SEP(1.2% for 1 to 9 classification) and R2=0.65. (needs work)

• Warner-Bratzler shear force (WBSF) – longissimus thoracis steaks > 79% correct classification. WBSF SEP(1.2kg for 2 to 11.7kg) with R2=0.67, RMSECV=1.3kg.

Examples - Meat (Beef)…………….• Cooking end-point temperature (EPT) – critical for safe

consumption of beef. Temp (high, long time) = not palatable. Temp (lower, shorter time) = increase food piosoning. EPS SEP(0.74degC ) with R2=0.97 using 400-2500nm.

• Beef adulteration with lamb, pork, skim milk powder, wheat flour.

• Distinguishing frozen-then-thawed then minced– 100% correct classification of frozen-thawed samples, 19% error for fresh samples.

• Microbial spoilage – PH influences microbial growth, 1413 and 1405 cm-1 identified as peaks indicative of amide-CN due to protein degradation by microorganisms.

Examples - Meat (Pork)• Intact sausages measuring fat, moisture and

protein – Fat SEP 1.47% with R2=0.98, moisture SEP 0.97% with R2=0.93, and protein SEP 1.08% with R2=0.97.

• Fatty Acid Composition • PH – SEP(0.1 for 5.3 to 6.7) with R=0.73,

RPD=0.25/0.1 = 2.5, 1000-2630nm.• Water Holding Capacity (WHC) – SEP(1.8% for

0.7 to 8%) with R=0.84. NIR reflectance able to correctly classify samples <5% or >7% WHC.

Examples - Meat (Chicken)• PH, colour, shear force, tough and tender

classification of cooked and raw meat using Vis/NIr.

• Fecal contamination on chicken skins using Vis/NIR

• Microbial spoilage, total viable counts using FTIR/Machine learning.

Examples - Dairy (Milk)• Inline during milking to predict fat, protein,

lactose, somatic cell count and milk urea nitrogen. Achieved R2 between 0.82 and 0.95 with standard errors between 0.05 and 1.33.

• Protein, fat, casein, whey protein, lactose, dry matter for raw milk.

• Fat, protein, dry matter for processed milk.

Examples - Dairy

Cheese• Dry Matter, Fat, moisture• Cholesterol (Paradkar et al 2002)Butter• Moisture, saltPowder • Water, fat, protein, lactose.

Examples

Margarine (inline)• Moisture RMSECV 0.3% (weight) with

R2=0.998 (780-1100nm)Honey• Adulteration with fructose and glucose. Pure

honey’s correctly identified 99% of the time.Coffee• Discriminate between normal and

decaffeinated.

Examples

Cereals• Flour quality (hardness) although calibration

responded to granular size.• Protein, moisture. Used inline on harvesters.Paper• Determining pulp yield and kappa number for

kraft pulp and black liquor samples.

Future

NIR chemical imaging gives the ability to quantify a chemical component and also provide spatial resolution. – 2D spectroscopy

Future…………..• Fluorescence Spectroscopy• Microbial, bacterial quantification.