PARTICLE SIZE DETERMINATION USING NEAR-INFRARED SPECTROSCOPY

Darin Starkey and Ryan Hicks

800.365.1357

115 Executive Drive, Highland, IL 62249www.trouwnutritionusa-pets.cominfo@trouwnutrition.com

Abstract The ability of the near-infrared (NIR) reflectance spectrophotometer was evaluated for the determination of the particle size inclusion percentage in ground rice. The fraction by weight of particles sized 600-850 µm was measured as the variable of interest throughout the experiment. Reference data for this variable was obtained through a unique sieving procedure.1 The sieving data was matched with a population of spectra, which was obtained through the NIR spectrophotometer. An optimized calibration model was established and appropriately validated. The R-Squared (RSQ), Standard Error of Cross Validation (SECV), and 1 minus Variance Ratio (1-VR) of the optimized calibration were 0.884, 0.625, and 0.855, respectively. Results of the validation show a Standard Error of Prediction (SEP) of 0.496, with a sieve test Standard Error of Difference (SED) of 0.238. These results indicate that the calibration model is a suitable replacement for the manual sieving procedure.

IntroductionIn the companion animal food industry, there is a large demand for the accurate knowledge of the physical and chemical properties of all ingredients that comprise pet food. These materials are held at strict tolerances by the industry.2 Many of these ingredients contain variations in particle sizes and shapes. Therefore, it is crucial to develop a method to ensure the compliance of these physical and chemical properties of the ingredients to make the desired-end pet food product. Consequently, near-infrared (NIR) spectroscopy is fast becoming an important technique for the analysis of ingredients in the pet food industry, since the NIR spectra are quickly measured directly on the surface, without destroying the sample with any pretreatments.3

Additionally, chemo-metrics provides an ideal method of determining quantitative information (such as particle size) using the NIR spectra of multi-component analysis.4 Multiple multi-component analysis techniques are available for use and have been proven to be successful in extracting the correct information from NIR spectra. Techniques, such as multiple linear regression (MLR), principal component analysis (PCA), and partial least squares (PLS), are commonly used.5

Studies have shown that NIR spectrophotometers are able to predict particle size with repeatable accuracy.6-8 According to these studies, a successful calibration must account for all expected variations in the library of spectra.9 Also, choosing the best combination of calibration parameters specific to the measured variable is critical to the accuracy of the prediction, as well as correctly optimizing the calibration method to a legitimate validation study.

Objective The objective of this study is to evaluate the capability of NIR spectroscopy in determining the composition of 600-850 µm particles in a representative ingredient used in the pet food industry (in our case we chose ground rice). Ideally, this study will lead to the swift and accurate development of particle size determinations in many other common ingredients used in the pet food industry by applying the chemo-infometrical NIR spectroscopy methods used here.

ExperimentalNIR Instrumentation

The spectra for the calibration models were recorded with an XDS Rapid Content Analyzer spectrophotometer. The available wavelength range is 400-2500 nm. ISIScan and WinISI software are used to analyze all data.

Manual Sieving Instrumentation

All samples were manually sieved for five minutes using a W.S. Tyler Ro-Tap® Model RX-29, Serial Number 10-1067. A.S.T.M. U.S.A. Standard Testing Sieves were used for analysis. In particular, the U.S. standard 30 mesh sieve is used for the analysis of ground rice samples, as the specification of ground rice is centered around the U.S. standard 30 mesh sieve.

Sampling

A critical factor when performing analyses for ground rice is to obtain a representative sample of the product. All samples were obtained according to local standard operating procedures.1

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Building a Library/Population Structuring

A successful calibration uses a library of spectra that accounts for all expected variation.8,9 The variations considered for this study were NIR cup packing error, sample consistency, seasonal changes, and multiple suppliers. To combat cup packing and particle consistency variations, three to four scans of all ground rice lots were taken from different parts of a given sample, with each test using a clean NIR cup. Seasonal and supplier variation were represented using ground rice samples from every month of the past year from multiple suppliers.

A wide variety of the percentages of 30 mesh particles were needed for the calibration to be robust. This was achieved by adding and/or subtracting particles of this

Figure 1. Final population spectra of 225 scans obtained from the XDS NIR spectrophotometer.

Figure 2. Histogram of lab-determined values for the percentage of ground rice, which does not pass through a U.S. 30 mesh sieve.

size to other samples and mixing for a standard time of one minute. These fabricated samples would then be NIR tested before a sieve test was taken. This variation was repeated for multiple samples.

Lab sieve test data was added to the initial library of 250 spectra. The library was analyzed using the WinISI software, which gave score values using principle component analysis (PCA). Based on the scores of the analysis and 3D viewing of the principle components, outlying spectra were removed accordingly. Also, spectra were removed that caused deviation to a normal population distribution. The final population of 225 is represented in Figures 1 and 2.

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Optimization of the Calibration Model

The development of the best calibration model required a unique combination of settings for wavelength range, scatter correction, math treatment, and regression methods. This was achieved through trial and error along with guidance from correlation statistics and previous studies.6-8 In the first optimization attempt, scatter correction and wavelength range were varied while keeping the default settings of “1, 4, 4” and “PLS” for math treatment and regression method, respectively. All calibration model options are shown in Table 1. All calibration models were compared using output R-Squared (RSQ), Standard Error of Cross Validation (SECV), and 1 minus Variance Ratio (1-VR). The first set of Calibration outputs is shown in Table 2.

A scoring system was established in order to evaluate the calibration models, shown in Eq. (1). Optimum wavelength and scatter correction settings were used to find the best math treatment and regression method. Multiple iterations of this method were completed. Adjustments of the settings became smaller and smaller until a final optimized calibration model was used for validation.

Parameters Op�ons/Range Op�ons Used

Wavelength Region 400-2500nm All

Sca�er Correc�on SNV & Detrend, SNV, Detrend, Standard MSC, Weighted MSC, Inverse MSC, None (No math treatment) All

Math Treatment 1st term (deriva�ve

number) 1-4 All

Math Treatment 2nd

term (# of points for deriva�ve)

1-99 1-15

Math Treatment 3rd term (# of smoothing

points) 1-50 1-15

Regression Method Modified PLS, PLS, PCR All

Table 1. Calibration model parameter options.

Eq. (1)

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Sca�er Correc�on/Wavelength Range(nm) RSQ SECV 1-VR 408-1092, 1108-2400

SNV & Detrend 0.857 0.677 0.825 SNV 0.864 0.686 0.825 Detrend 0.862 0.673 0.830 Standard MSC 0.866 0.685 0.826 Weighted MSC 0.859 0.686 0.825 Inverse MSC 0.859 0.685 0.826 None 0.877 0.679 0.824

1108-1452 SNV & Detrend 0.752 0.855 0.726 SNV 0.709 0.901 0.697 Detrend 0.754 0.824 0.747 Standard MSC 0.709 0.901 0.696 Weighted MSC 0.709 0.900 0.697 Inverse MSC 0.709 0.900 0.697 None 0.767 0.822 0.744

408-1096 SNV & Detrend 0.839 0.738 0.793 SNV 0.857 0.707 0.810 Detrend 0.847 0.744 0.791 Standard MSC 0.850 0.713 0.806 Weighted MSC 0.846 0.725 0.799 Inverse MSC 0.855 0.709 0.808 None 0.851 0.716 0.801

508-720, 900-1092, 1108-1452 SNV & Detrend 0.865 0.665 0.835 SNV 0.843 0.723 0.798 Detrend 0.843 0.687 0.820 Standard MSC 0.852 0.696 0.811 Weighted MSC 0.870 0.657 0.831 Inverse MSC 0.835 0.801 0.752 None 0.875 0.692 0.817

408-1092, 1108-1452 SNV & Detrend 0.858 0.670 0.831 SNV 0.850 0.686 0.822 Detrend 0.866 0.657 0.836 Standard MSC 0.841 0.698 0.816 Weighted MSC 0.838 0.697 0.817 Inverse MSC 0.851 0.679 0.826 None 0.853 0.672 0.830

Table 2. Comparison of PLS Calibration Models for 600-850 µm particles.

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Optimized Calibration Model

According to the Scoring system, the best wavelength regions were “408-1092, 1108-1452 nm” and “408-1092, 1108-2400 nm”. This was expected due to calculations showing the highest correlation to 600-850µm particles in the 408-750 nm and 1100-1500 nm wavelength ranges. Furthermore, wavelength regions of only “408-1092 nm” or “1108-1452 nm” received worse scores than when they were combined. There are consistent studies which have found particle size is expressed throughout the whole spectrum with combined color/NIR regions having the best predictability of particle size.6,7 The final regions that showed the best score were 408-720, 900-1092, 1108-1320, 1420-1488 nm.

The best scatter correction settings were “Detrend” and “None”. “None” refers to not using any scatter correction. Not using a scatter correction matched well with math treatments of the 2nd derivative. The final math treatment used in the calibration was 2, 8, 8. The first term shows that the second derivative was used for math treatment. It was observed that better calibration scores were produced by using the 2nd instead of the 1st derivative. Similar findings were observed in a study with wheat particles >1041 µm.7

Wavelength Range Math Treatment Regression Method 408-720, 900-1092, 1108-1320,

1420-1488 2,8,8 Modified PLS

RSQ SECV 1-VR Sca�er Correc�on 0.884 0.6251 0.8554 None

Validation

Ten samples, which were not included in the calibration, were used to validate the optimized calibration model. The SEP of the model was found and compared to the SED of the sieve test. The SED was found with results from four tests ran by as many testers. The calculation method used for SED and SEP are shown in Eq. (2, 3). The results of the calculations are shown in Tables 4 and 5.

The SED and SEP were calculated at 0.238 and 0.496, respectively. These values show this calibration model to have accurate prediction capability. 9 The manual states that the ratio of SEP/SED should lie within 1.5-2.0 as a “Rule of Thumb” indicator of a good calibration.9 The ratio for this calibration was calculated at 2.08, which is very close to the 1.5-2.0 range given.

Another study used relative standard error (RSE) as a scoring method for calibration models and their respective reference data.8 This method proportions the error as a percentage of the mean reference value. The RSE for the reference sieve method and NIR prediction was calculated at 7.37% and 15.36%, respectively. The ratio of both RSE’s was the same as the SEP/SED previously stated at 2.08.

Table 3. Optimized Calibration Model.

Results and DiscussionThe objective of this experiment was to accurately predict the mass percent of particles 600-850 µm in ground rice using NIR Reflectance data and ultimately replace the required sieve test with the NIR test. A final calibration model (Table 3) was found using the scoring system shown in Eq. (1). The validation of this calibration model shows a Standard Error of Prediction (SEP) of 0.496% with a sieve test Standard Error of Difference (SED) of 0.238%.

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Table 4. SED for labsieve tests.

Eq. (3) Testave is the average sieve testing value, NIRT1, NIRT2 are the first and second NIR tests, and N is the total number of samples.

Eq. (2) Davg is the average difference between tests and N is the total number of samples.

Table 5. SEP for NIR calibration.

600-850 µm Par�cles in Ground Rice (% Mass)

VS# Test 1 Test 2 Test 3 Test 4 Davg 1 3.082 3.055 3.101 3.372 0.161 2 3.143 3.216 2.985 3.256 0.148 3 3.023 3.075 3.141 3.342 0.170 4 5.529 5.458 5.473 5.915 0.238 5 3.117 3.081 2.939 3.559 0.316 6 2.019 1.906 1.740 2.279 0.288 7 1.931 1.751 1.995 2.276 0.273 8 1.369 1.184 1.138 1.603 0.264 9 1.866 1.735 1.882 1.970 0.120

10 4.461 4.175 4.759 4.531 0.304 SED = 0.238

Davg = The average difference between tests VS#= Valida�on Sample Number

600-850 µm Par�cles in Ground Rice (% Mass)

VS# Testave NIRT1 NIRT2 D1 D2 1 3.153 3.435 3.186 0.282 0.033 2 3.150 3.393 2.924 0.243 -0.226 3 3.145 2.963 3.248 -0.182 0.103 4 5.594 5.436 5.516 -0.158 -0.078 5 3.174 2.667 2.568 -0.507 -0.606 6 1.986 2.792 2.033 0.806 0.047 7 1.988 2.841 3.076 0.853 1.088 8 1.323 1.925 1.982 0.602 0.659 9 1.863 1.605 1.941 -0.258 0.078

10 4.482 3.941 3.922 -0.541 -0.560 SEP = 0.496

Testave= The average tes�ng valueNIRT1, NIRT2= The first and second NIR testsD1, D2= The difference between predic�on and the average lab testVS#= Valida�on Sample Number

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Sensitivity to Fabricated Samples

Validation samples 6, 7, and 8 were the only fabricated samples out of the validation set. These were fabricated using multiple ground rice lots and random proportions of <250, 250-400, 400-600, and >850 µm sized ground rice particles. The particle size distribution was most likely not the distribution of an average manufactured ground rice lot. These samples were added into the validation set to show the robustness of the prediction capability and the effects of other sized particles on the prediction. As seen in Table 4, the fabricated samples show the highest prediction errors. When removing these three samples from the validation set, the SEP/SED ratio and the validation RSE become 1.43 and 10.6%, respectively. It should be mentioned that the fabricated samples included in the library were only altered by particles 600-850 µm in size. The natural particle size distribution was only minimally disturbed.

Complications with Calibration Model

As previously stated, multiple NIR tests for each ground rice sample were used in the calibration library to account for expected errors and consistency differences. This may have caused the SECV and the 1-VR to show better results than if only one test was taken for each lot of ground rice. This is due to the cross-validation method, which uses a random group of samples to validate other samples. With multiple spectra for the same sample included in the population, spectra from the same sample will be used to validate each other. There is a better

chance that the spectra in a validation set will be explained if the calibration includes spectra from the same sample. This reduces the validation method’s credibility, which is another reason for validating with samples not included in the calibration.

A unique feature of the calibration model is the disproportionate addition of spectra in the >5% granules of size 600-850 µm. These were added for better recognition of ground rice lots that would fail the local specifications of >4%. Furthermore, to offset these added spectra, spectra were added to the library of <2% until the average in the library narrowly matched the natural average.

ConclusionsAs shown in this work, a NIR reflectance spectrophotometer can accurately predict particle size percentage in ground rice. A strong library of spectra was built that represented all expected variations, as well as the natural distribution of the variable. With the robust library, a calibration model was optimized with the combination of settings for wavelength region, scatter correction, math treatment, and regression, which explained the variable the best. This calibration model was then validated using 10 samples that were not included in the calibration. The relative SEP for the calibration model was 15%, and reduces to 10% when the validation excludes fabricated samples. Furthermore, it is conceivable that NIR analysis is an effective replacement for manual sieve testing for this ingredient.

References1) Trouw Nutrition International Sieve Test Procedure. Referenced by W.S. Tyler™ Test Sieve Analysis Procedure, Accessed 20Oct11.

2) Best, T.; Project Discovery presentation. August 2011.

3) Iwamoto, M.; Kawano, S.; Uozumi, J.; Introduction of Near-Infrared Spectroscopy. Saiwai Shobou, Tokyo, Japan, 1994.

4) Martents, H.; Naes, T.; Multivariate Calibration. John Wiley and Sons, New York, 1989.

5) Otsuka, M.; Chemoinformetrical evaluation of granule and tablet properties of pharmaceutical preparations by near-infrared spectroscopy. Chemometric and Intelligent Laboratory Systems, 82 (2006) 109-114.

6) Pasikatan, M.C., Steele, J.L., Spillman, C.K., Haque, E.; Review of Near infrared reflectance spectroscopy for online particle size analysis of powders and ground materials. J. Near Infrared Spectrosc. 9 (2001) 153-164.

7) Pasikatan, M.C., Steele, J.L., Spillman, C.K., Haque, E.; G. A. Milliken Evaluation of a Near-Infrared Reflectance Spectrometer as a Sensor for First-Break Ground Wheat: Studies with Hard Red Winter Wheats. Cereal Chemistry. 79 (2002) 92-97.

8) Blanco, M., Peguero A.; An expeditious method for determining particle size distribution by near infrared spectroscopy: Comparison of PLS2 and ANN models. Talanta. 77(2008) 647-651.

9) Gell, A.; Foss ISIScan and WinISI Software Certification Training presentation. Observed June 2010.

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