feasibility study on the rapid compositional … study on the rapid compositional analysis of hog...

Feasibility Study on the RapidCompositional Analysis of HogManure by Near-infraredSpectroscopy

August 1999

Report to THE MANITOBA LIVESTOCK MANUREMANAGEMENT INITIATIVE INC. on Project 99-01-25

by D. F. Malley and L. Vandenbyllaardt

PDK Projects, Inc.365 Wildwood ParkWinnipeg MB R3T 0E7www.pdkprojects.com

in collaboration with G. Racz1, M. Fitzgerald1, G. Plohman2 and J. Hicks3

1 Department of Soil Science, University of Manitoba2 Elite Swine Ltd., Manitoba 3 Norwest Labs, Winnipeg

Executive Summary

This study added a near-infrared spectroscopy (NIRS) dimension to a studyunderway funded by the Agricultural Research Development Initiative on long-termeffects of hog manure on soil quality and productivity. The present study utilizedsamples of hog manure collected and analyzed by Norwest Labs in the summer and fallof 1998 to expand knowledge of the applicability of NIRS for the rapid analysis of hogmanure.

The present study expanded on the MLMMI Project 98-01-15 reported byMalley and Currie (1999) in three ways. Firstly, it repeated exploration of the feasibilityof developing useful NIR calibrations for nutrients, N, P, and K in hog manure. Secondly, it expanded the sources of variability in the sample set from 7 to 25 hogmanure ponds. Thirdly, it greatly expanded the constituents examined from physicalparameters, nutrients and salts to include 27 metals and minor elements.

The 75 samples in this study were analyzed by Norwest Lab for % moisture,electrical conductivity, NH4-N, N, P, K, Na, Ca, Mg, Cl, and a suite of elementsincluding Ca, Mg, K, Na, P, Al, Sb, As, Ba, Be, Bi, B, Cd, Cr, Co, Cu, Fe, Pb, Li, Mn,Mo, Ni, Se, Si, Ag, Sr, S, Ti, Tl, V, Sn, and Zn. The elements were determined byinductively-coupled plasma emission spectrometry. Because the samples were stored forseveral months before being scanned by NIRS, they were re-analyzed in the FreshwaterInstitute Analytical Laboratory for NH4-N, total dissolved N, suspended N, totaldissolved P, soluble reactive P, suspended P, and suspended C.

Samples were scanned using a Foss NIRSystems Inc. Model 6500 visible andnear-infrared scanning spectrophotometer from 400 to 2500 nm using Near-infraredSpectroscopic Analysis Software (NSAS). Samples were scanned in a watertight cellwith a path length of 2 mm in the transflectance mode. The cell was scanned in a staticmode and in a spinning cup module. Calibrations were developed between the spectraldata and the chemical data for each constituent using the multiple linear regressionoption of the NSAS software and evaluated statistically.

Useful calibrations were developed for NH4-N, TDN, Suspended N, Total N,TDP, SRP, Suspended P, Total P, Suspended C, Ca, Mg, Ba, Be, Cd, Cu, Cr, Fe, Mn,Mo, Ni, Se, Sr, S, Ti, V, and Zn. Useful results were not obtained for Na, K, Cl, Al, As,B, and Li. The calibrations developed for the nutrients and salts were not good asachieved by Malley and Currie (1999). The difference was attributed to the greatervariability in the samples in this study, representing 25 ponds compared with 7 ponds inthe earlier study.

Analysis of the constituent and spectral data by Principal Component Analysisdemonstrated that this may be a useful tool for sample selection for the development offuture robust calibrations for field use.

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AcknowledgmentsThis study was made possible or assisted by several agencies and individuals

whose assistance the authors gratefully acknowledge.

The study was funded by the Manitoba Livestock Manure Management InitiativeInc. (Project 99-01-25).

This study extended Agricultural Research Development Initiative (ARDI)Project 98-124 in progress by G. Racz, J. Hicks, and G. Plohman entitled “Long-termeffects of hog manure on soil quality and productivity”. The samples and the chemicalanalytical data provided by Norwest Laboratories to the present study originated fromthe ARDI-supported study. Our thanks to Barb Steeves-Hind of Norwest Labs forproviding the manure samples and analytical data.

Some additional chemical analyses of hog manure were performed in theDepartment of Fisheries and Oceans’ Freshwater Institute Analytical Laboratory by PaulMarten and Jacques Lagasse under the supervision of Michael Stainton.

Near-infrared spectroscopy was performed in the Freshwater Institute. FossNIRSystems Inc. (Silver Spring, MD) is acknowledged for in-kind support withinstrumentation and technical support.

Multivariate analysis software, Unscrambler®, was purchased with funds from agrant from the Manitoba Rural Adaptation Council’s Research and DevelopmentProgram, April - September 1999.

The photo on the title page is of the hog operation of M. Klaussen near Linden,MB.

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Table of ContentsExecutive Summary.............................................................................................................iiAcknowledgments..............................................................................................................iiiIntroduction..........................................................................................................................1Near-infrared Spectroscopy (NIRS).....................................................................................2

Sample Requirements for NIRS Calibration..........................................................3Study Design........................................................................................................................4Methods................................................................................................................................5

Chemical Analysis .................................................................................................5Near-infrared Spectroscopy....................................................................................5

Recording Spectra from Manure Samples..................................................5Calibration Procedure................................................................................6Statistical Evaluation of Calibrations........................................................6

Principal Component Analysis...............................................................................7Results..............................................................................................................................8

Manure Composition .............................................................................................8Intra-laboratory and Inter-laboratory Comparisons................................................9Principal Component Analysis of Manure Composition ................................13NIR Spectra..........................................................................................................16

Principal Component Analysis of Spectra .........................................................18Prediction of Constituent in Manure by NIRS.....................................................20

Discussion.........................................................................................................................27References.........................................................................................................................29Appendix A.......................................................................................................................31

Rapid Analysis of Hog Manure 1

Introduction Economically and environmentally-sustainable operation of the hog

production industry in Manitoba requires a life cycle approach to the managementof all inputs and outputs. Acceptable management of the manure wastes from theoperations is a particular challenge not only because of the associated odour, butalso because of their large volume and high water content. The practice of usingthe manure as a raw material as a crop fertilizer can be ecologically-sound, bothsolving a waste management problem and reducing the cost of chemical fertilizer,providing precautions are taken to avoid over-fertilization of soils; leaching ofnutrients into ground and surface water; and accumulation of salts, heavy metals,and minor elements in the manure by the soil.

Determination of constituent concentrations in the hog manure is desirablebecause the concentration of total solids and the chemical composition of themanure vary from one production facility to another, over time within a facility,and most importantly, with settling. Material drawn from the top of a truck, tank,or lagoon will be highly liquid and contain high ammonia and low phosphateconcentrations, while that from the bottom can be highly concentrated inphosphate, organic nitrogen and other particle-bound constituents. Thus, one-time mixing and sampling of manure to be applied to fields will not providecompositional information on nutrient application rate to the field with time,unless the manure is constantly mixed as it is applied.

A method of chemical analysis of the manure that is rapid, cost effective,and, if possible, field-portable and operating in-stream would have wideapplicability. This study extends that of Malley and Currie (1999) that exploreda new use for an existing analytical technology, i.e., near-infrared spectroscopy(NIRS). This study repeats the NIR analyses of N and P and salts examined byMalley and Currie (1999) and extends the constituents examined to includenumerous metals and minor elements.


Near-infrared Spectroscopy (NIRS)Near-infrared spectroscopy is a 30-year old technology that has the

capability of determining quantities of organic constituents in liquids, slurries,and solids. It is used globally for determination of a wide variety of constituents,composition, and functionality in agricultural products, feeds, food, forages,petrochemicals, cosmetics, polymers, (including waste plastic streams forrecycling), pharmaceuticals, textiles and other materials. Medical andenvironmental applications are emerging areas.

The technique is based on measurement of the intensity of the absorptionof near-infrared radiation (780 to 2500 nm) by a sample. Radiant energy in thisrange may excite molecular vibrations to higher energy levels. Absorbanceoccurs at wavelengths that match the frequency of the molecular vibrations. Commonly, NIRS is used for quantitative measurement of constituents effects onwater. Predictability of six heavy metals in sediments by NIRS containingorganic functional groups, such as covalent O-H, C-H, N-H, C-O, and C-N andfor prediction of functional properties of samples based on interaction of theseconstituents. Nevertheless, NIRS has been successfully used for thedetermination of inorganic substances, such as NH4-N in an industrialEscherichia coli fermentation broth (Hall et al. 1996). It has been used tosimultaneously determine the concentration of electrolytes such as NaNO3,NaNO3, Na2CO3 in liquids based on the perturbation of the water spectrum(Espinoza et al. 1999). Metals, themselves, do not absorb NIR light, but may bepredictable by NIR by their association with NIR absorbers or possibly by theirwas attributed to their association to naturally-occurring organic matter (Malleyand Williams, 1997).

NIRS is not a stand-alone analytical technique. Its ability to provide rapidanalyses depends on the prior preparation of mathematical calibrations used topredict constituents, parameters or functionality in unknown samples. Calibrations are based on statistical relationships developed between NIR spectraof a set of samples and values for constituents, parameters or functions of interestthat have been determined by conventional methods on the same samples. Thecalibrations are then entered into the instrument’s computer and are used topredict the parameters of interest in the unknown samples within 2 minutes orless, depending on the NIR technology used. Accuracy and precision aremaintained by periodical, on-going analysis by the conventional methods of asub-sample, e.g., 5-10%, of the unknown samples.

NIRS has the capability of measuring constituents in liquids (e.g., Gatin etal. 1995) and slurries (Wust et al. 1996, Malley and Currie 1999) through the useof one of two methods, transmission and reflectance, or their combination, knownas transflectance. NIRS technology is used in-line in industrial applicationswhere fibre optic probes are inserted into the industrial process stream (e.g.,Brookes et al. 1996), or focussed on to an industrial flow such as waste plastic


articles.

Sample Requirements for NIRS Calibration:

The sample requirements for developing an NIR calibration are:

1. that there is a minimum of 50 samples, but preferably 100-150 samplesthat have been analyzed for the constituents or parameters of interest byconventional methods with a high degree of accuracy

2. that samples represent a range of values in each constituent, e.g., highestvalues should be at least twice and preferably ten times the lowest values

3. that samples represent the full range of concentration of the constituents ofinterest anticipated in future samples to be predicted by the calibration

4. that samples represent the range of physical and chemical compositionanticipated in future samples to be predicted by the calibration

5. that samples are chemically unchanged between the time they are scannedby NIRS and analyzed chemically by reference methods

6. that samples are physically uniformly mixed so that the aliquots analyzedby reference methods and those scanned by NIRS are truly representative.


Study Design The purpose of this study was to explore the feasibility of using NIRS for

the rapid, accurate analysis of 75 hog manure samples that had been subject tochemical analysis for a range of physical parameters, nutrients, salts, andelements, including Al, Sb, As, Ba, Be, Bi, B, Cd, Cr, Co, Cu, Fe, Pb, Li, Mn,Mo, Ni, Se, Si, Ag, Sr, S, Ti, Tl, V, Sn, and Zn. NIRS feasibility was assessed bystatistically evaluating the predictive capability of calibrations developed betweenthe NIR spectra and the constituent data.

Additional statistical analysis of the spectral and constituent data wasperformed using multivariate analysis software, Unscrambler® (CAMO ASAOslo, Norway).

The manure samples in this study were aged and not freshly-collected asin the study by Malley and Currie (1999). Between the time of collection frommanure ponds during the late summer and fall of 1998 and March 1999 when thesamples were obtained for this study, they were stored at 4oC.

Samples can change during storage and sub-sampling. For example,dissolved forms of nutrients may increase at the expense of the particulate forms,and gaseous NH3 may escape as sample bottles are opened for removal of sub-samples for analysis. Therefore, in the present study, nutrients, N and P, were re-analyzed, and suspended C was determined near the time of NIR scanning. Aswell, this allowed for comparison of NIR results for dissolved and suspended N;dissolved, soluble reaction (inorganic), and suspended P; and suspended C,between this study and that of Malley and Currie (1999)

The study by Malley and Currie (1999) demonstrated that transflectance isa suitable method for scanning manure samples in the NIR region. Intransflectance, the source of light and the NIR detector are on the same side of thesample. Light that reaches the detector includes both that which is reflected fromthe sample (termed diffuse reflectance), and that which passes through the sampleand is reflected back from the opaque ceramic disc on the far side of the sample. The path length (thickness of the sample) must be constant for all thetransflectance spectra within a calibration.

Sample presentation in this study was the same as that used by Malley andCurrie (1999). The sample cell was in a horizontal, static position duringscanning. The samples in the present study had a greater range of particulatesthan those in the Malley and Currie (1999) study. For reasons given below, anadditional sample presentation method, the spinning cup module, was used aswell in the present study. Calibrations developed from spectra recorded by eachmethod are compared below.


Methods

Chemical Analysis

The 75 manure samples in this study were analyzed by Norwest Labs byfour tests: 1) liquid manure physical properties - % moisture, electricalconductivity; 2) liquid manure nutrients - total N, NH4-N, total P, K and Na; 3)salts - Ca, Mg, Na, Cl, and 4) inductively-coupled plasma (ICP) spectroscopy for32 elements including Ca, Mg, K, Na, P, Al, Sb, As, Ba, Be, Bi, B, Cd, Cr, Co,Cu, Fe, Pb, Li, Mn, Mo, Ni, Se, Si, Ag, Sr, S, Ti, Tl, V, Sn, and Zn. Methods aredescribed by Racz et al. (in prep).

Some of the elements were present below the limits of detection for all ormost of the samples. Antimony, Bi, Ag, and Tl are not considered further here.

The 75 samples were analyzed in the Analytical Laboratory of theFreshwater Institute for NH4-N, total dissolved N, suspended N, soluble reactivephosphorus (SRP), total dissolved P, suspended P, and suspended C using themethods of Stainton et al. (1977). Subsamples were diluted 10,000 to 100,000times for the analyses. Total N was calculated from the sum of total dissolved Nand suspended N. Total P was calculated from the sum of total dissolved P andsuspended P.

Near-infrared Spectroscopy

Recording spectra from manure samples

NIR spectra were recorded using a Foss NIRSystems Inc (Silver SpringMD) Model 6500 visible/NIR scanning spectrophotometer and Near-infraredSpectral Analysis Software (NSAS). Manure subsamples were well shaken andaliquots removed and dispensed into a liquid sample cell with quartz glass on twosides, an opaque ceramic fastened over one glass side, and a gasket to make thecell watertight. The NIR instrument was equipped with a standard sampletransport but was turned on its back so that the transport operated horizontally,instead of vertically as is normal (shown in Malley and Currie, 1999). In this way,any settling particles stayed in the path of the light, rather than falling out of it.

Absorbance was recorded every 2 nm from 400 to 2500 nm. Between eachsample scan, a non-absorbing reference ceramic was scanned and the referencespectrum was automatically subtracted from each sample scan. Sub-samples wereloaded into the cell twice. For each loading, triplicate scans were recorded, withthe cell being turned 120o between scans.

For reasons given below, a second sample presentation module, thespinning cup, was utilized as well. The sample cell was the same as used with thestandard sample transport. The cell was loaded twice with manure as above, and


each loading was scanned in triplicate. A reference spectrum was obtainedbetween each scan of the manure sample as above.

Calibration Procedure

Triplicate spectra for each subsample were averaged to give one spectrumper subsample. The reference chemical results for all the constituents for eachsample were added to the NIR spectral file. For each constituent, the spectra weresorted from lowest to highest TPH values and divided into two sets, theodd-numbered spectra (set A) and the even-numbered spectra (set B). Each setrepresented the full range of concentrations. Using set A, about 150 calibrationequations were developed over wavelengths of 400-2498 nm using the stepwisemultiple linear regression (MLR) option in the NSAS software. Separatecalibration equations were computed using the raw optical data (log 1/R)smoothed over 4, 10, 20 or 40 wavelength points, and using the first or secondderivatives of the smoothed log 1/R data with several combinations of segment(smoothing) and derivative ("gap") sizes. Each calibration equation developedfrom set A was used to predict the constituent values for the independent spectrain set B. For each calibration equation, the NIR-predicted values for set B werecorrelated to their measured values.

The calibration was completed when one equation was selected as givingthe best results. The best calibration is the one with the highest r2 (coefficient ofdetermination) and lowest SEP (standard error of performance, i.e., the standarddeviation of the points about the 1:1 line). Other statistics used to evaluate thecalibration are the RPD, ratio of the SD of the reference chemistry values for theprediction set to the SEP, and the RER, ratio of the range of the referencechemistry values for the prediction set to the SEP. The process was repeatedreciprocally by using set B to generate equations that were tested on set A. In thisway, the constituent in all the samples was predicted by NIRS.

The procedure was repeated for each constituent.

Statistical Evaluation of Calibrations

The usefulness of NIRS for the determination of these parameters inmanure and soil was evaluated separately for each constituent in each matrix. Inthe successful analysis of agricultural commodities, usually r2 is >0.95, RPD is >5,and RER is >20. Nevertheless, in more variable samples such as manure or soil,values of r2 >0.9, RDP >3 and RER >10 are considered to indicate goodcalibrations.

Principal Component Analysis

Using multivariate analysis software, Unscrambler® (CAMO ASA Oslo,Norway), principal component analysis (PCA) was performed on the Norwest Lab


chemical data. The data were centred, all 36 constituents were weighted using atransformation 1/SD. This indicates which constituents are most important inexplaining the variability within the constituent data.

PCA was performed separately on the spectral data. The data werecentred and all 1044 wavelengths were weighted equally. This demonstratesrelationships among spectra and shows which wavelengths are most important inexplaining the variability among the spectra.

The constituent and spectral data sets were combined in an PCA/PLSanalysis (PLS2) to determine the relationships between the spectral and theconstituent data.


Results

Manure Composition

Physical parameters and the chemical composition of the nutrients in the75 manure samples determined by Norwest Labs are given in Appendix A. Nutrients determined in the samples by the Freshwater Institute AnalyticalLaboratory are given in Table 1. The concentrations of the metals and minorelements determined by ICP are given by Racz et al. (in prep).

Table 1. Results of chemical analysis of the hog manure samples in this study bythe Freshwater Institute Analytical Laboratory.

Constituent Abbreviation Numberof

samples1

Concentration, g/L

Mean SD Min Max

ammonium-nitrogen

NH4-N 74 2.13 0.85 0.76 4.30

total dissolvednitrogen

TDN 74 2.25 0.89 0.95 4.70

suspendednitrogen

Susp N 74 0.63 0.68 0.009 3.74

total nitrogen(calculated)2

Total N 74 2.88 1.34 0.98 6.77

soluble reactivephosphorus

SRP 74 0.52 0.49 0.012 1.80

total dissolvedphosphorus

TDP 74 0.53 0.48 0.027 1.76

suspendedphosphorus

Susp P 74 0.41 0.53 0.015 3.35

total phosphorus(calculated)3

Total P 74 0.94 0.98 0.059 5.11

suspendedcarbon

Susp C 74 6.25 6.95 0.20 27.33

1 One sample was omitted because chemical analytical values were 2 times higherthan the next most concentrated sample2 Sum of suspended N and total dissolved N3 Sum of suspended P and total dissolved P


Intra-laboratory and Inter-laboratory Comparisons

The determination of several nutrients and salts by more than one methodwithin a laboratory and between laboratories allowed for a comparison ofchemical analytical results. Mean concentrations of NH4-N, total N and total Preported by the Freshwater Institute (Table 1) were slightly higher than thosedetermined by Norwest Labs (Appendix A). This could be due to differences inmethods or possibly could indicate that evaporation had occurred over the up to 6-month period of storage.

There was good agreement between the results from the two labs for NH4-N (r2 = 0.93) (Table 2, Fig. 1) and moderately good agreement for Total N (Table2, Fig. 1). Total P by two methods in Norwest Labs compared moderately well(Table 2, Fig.1). Total P determined in the Freshwater Institute agreed better withthe Norwest Labs ICP values, particularly at the lower concentrations (Table 2,Fig. 1) than with the Norwest Labs nutrient determination (Table 2, Fig. 1).

Potassium measured by the nutrient and ICP methods did not agree well(r2= 0.42) (Table 3, Fig. 2) largely because of two samples that had dissimilarconcentrations by the two methods. With these samples removed from thestatistical analysis, r2 improved to 0.75 (Table 3).

For Na, the concentrations measured by the salt and ICP methods agreedreasonably well (Table 3, Fig. 1). Agreement of these methods with the resultsfrom the nutrient method was improved when one sample was removed (Table 3;Fig. 2).

Calcium and Mg concentrations were each in good agreement by the twomethods (Table 3; Fig. 2). Mean concentrations for the salts method were higherthan for the ICP method for Ca and Mg and lower for Na (Fig 2).


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Fig. 1. Comparison of chemical analytical results from two laboratories or two chemical methods fornutrients. Upper left) NH4-N comparison between two labs; Upper right) Total N comparisonbetween two labs; Middle left) Total P comparison between two methods; Middle right and lowerleft) Total P comparison between two labs. The line is 1:1 and not the best fit regression line.


Table 2. Correlation between nutrient values determined by two methods within alaboratory or by two laboratories

Constituent Comparison Figure r2

NH4-N NWL vs FWI 1A 0.93

Total N NWL vs FWI 1B 0.86

Total P Nutr vs ICP 1C 0.88

Total P FWI vs ICP 1D 0.92

Total P FWI vs Nutr 1E 0.89

Table 3. Correlation between nutrient or salt values determined by two methodsin Northwest Labs

Constituent Comparison Figure r2 for AllSamples

Samplesremoved

r2 afterOutliersRemoved

K Nutr vs ICP 2A 0.42 424, 425 0.75

Na Nutr vs Salt 2B 0.72 393 0.93

Na Nutr vs ICP 2C 0.62 393 0.82

Na Salt vs ICP 2D 0.88

Ca Salt vs ICP 2E 0.91

Mg Salt vs ICP 2F 0.92

These comparisons focus on the fact that determinations of constituentconcentrations by conventional chemical analysis are associated with analyticalerror. Accurate evaluation of the performance of NIR calibrations requiresknowledge of analytical errors in both the reference chemical methods and theNIR predictions. This requires special planning so that analytical error isdetermined in blind replicates submitted to the analytical laboratory along with theunknown samples. Judging the success of calibrations against a goal of r2 = 1 isappropriate only for cases where reference chemistry is highly reproducible, suchas in uniform samples like grain or other commodities. Chemical analysis ofenvironmental samples or waste materials is usually less reproducible.


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Fig. 2. Comparison of chemical analytical results by several methods (nutrients, salts, and ICP) inNorwest Labs for Upper left K; Upper right and middle) Na; Lower left) Ca; and Lower right) Mg. The line is the 1:1 line and not the best fit regression line.


Principal Component Analysis of Manure Composition

Principal component analysis (PCA) of the constituent data for the 75samples in this study showed that the first principal component (PC) explained 63 % of the variance, the 2nd an additional 14 %, the 3rd, 6 % and the 4th, 5 %(Table 4). Sixteen PCs were required to explain 99% of the variance.

Table 4. Amount of variance in chemical composition of the 75 manure samplesin this study accounted for by the first 10 principal components

PrincipalComponent

Cumulative % VarianceAccounted For

1 63.33

2 77.16

3 82.78

4 87.58

5 90.32

6 92.29

7 93.73

8 95.06

9 95.90

10 96.63

Figure 3 is a two-dimensional scatter plot of scores for the first two PCs. The closer the samples are in the score plot, the more similar they are with respectto the first two PCs. Samples farthest apart are most different.

Figure 4 is a two-dimensional scatter plot of loadings for the constituentvariables. That is, it shows the importance of the different constituents for each ofPC1 and PC2. Figure 4 and Table 5 show that for this set of samples, PC1 isassociated with variation in N, P, Ca, Mg, and most of the elements. H2O is to theleft of the plot indicating an inverse relationship between % moisture and solids inthe samples. PC1 may be summarized as total solids. PC2 is salinity, withconductivity, Na, K, and Cl strongly associated with this component (Table 5). PC3 is associated with Al, Si, and Fe plus other metals (Table 5). It is interpretedas associated with clay minerals, such as from the incorporation of clay from thepond liners during mixing. PC4 appears to be a nutrient component, associatedwith NH4-N, N and K.


Figure 3. Score plot for the 75 manure samples in this study for the first twoprincipal components for chemical composition

Figure 4. Loadings of constituents in the 75 manure samples in this study on thefirst two principal components


Constituents to the right of Fig. 4 are variables which have high values forthe samples to the right in the score plot (Fig. 3). Thus, Fig. 3 shows that thegreatest concentration of samples is in the dilute, low-solids, and low salinitylower left quadrant. There are samples that are high in solids and not very salty(lower right quadrant) and samples that are high in salts but not very high in solids(upper half of the plot).

Table 5. Loadings of constituents as determined by Norwest Labs for the 75manure samples in this study on the first four principal components

Constituent PC1 PC2 PC3 PC4

% moisture -0.185 -0.0196 -0.0644 -0.0346

Conductivity 0.0150 0.379 -0.0553 0.268

Total N 0.162 0.166 -0.0518 0.255

NH4-N 0.111 0.264 -0.0281 0.358

Total P - Nutr 0.180 -0.0370 -0.0628 -0.0159

Total P - ICP 0.191 -0.0432 -0.136 -0.0204

Na - Nutr -0.0203 0.347 -0.0231 -0.305

Na - Salt 0.0185 0.345 0.0100 -0.409

Na - ICP -0.0017 0.341 0.0715 -0.390

K - Nutr 0.0725 0.255 -0.210 0.153

K -ICP 0.0596 0.319 -0.0002 0.317

Ca - Salt 0.184 -0.0485 -0.143 -0.0785

Ca - ICP 0.187 -0.0508 -0.115 -0.0314

Mg - Salt 0.191 -0.0289 -0.0532 -0.0149

Mg - ICP 0.184 -0.0379 -0.0523 0.0452

Cl 0.106 0.318 0.0.0853 -0.182

Al 0.162 -0.0499 0.379 0.0788

As 0.119 -0.112 -0.0274 -0.153

Ba 0.192 -0.0649 0.130 -0.0071

Be 0.121 -0.161 -0.119 -0.140

B 0.179 -0.0379 -0.0949 -0.146


Cd 0.198 -0.0518 -0.0184 -0.0503

Cr 0.196 -0.0795 -0.0170 -0.0827

Co 0.156 0.0227 0.240 0.0190

Cu 0.183 -0.0321 -0.107 0.104

Fe 0.187 -0.0648 0.224 -0.0050

Pb 0.115 -0.166 -0.111 -0.160

Li 0.126 -0.0910 0.446 -0.0472

Mn 0.193 -0.0569 -0.143 -0.0535

Mo 0.181 -0.0032 -0.245 -0.0154

Ni 0.188 -0.0604 -0.0120 -0.0595

Se 0.188 -0.0029 -0.122 -.0480

Si 0.157 -0.0282 0.369 0.0893

Sr 0.184 0.0356 0.0615 0.0532

S 0.192 0.0646 -0.0800 0.0448

Ti 0.191 -0.0607 0.176 -0.0776

V 0.188 -0.0923 0.0229 -0.0776

Sn 0.192 -0.0530 0.109 0.0511

Zn 0.174 -0.0166 -0.266 -0.083

NIR Spectra

Transflectance spectra of samples of thick hog manure are shown in Fig. 5. Fig. 5 upper shows triplicate spectra recorded sequentially over a 6 to 7 minuteperiod using the standard transport sample presentation. Fig. 5 lower showstriplicate spectra of a different sample than in 5 upper recorded using the spinningcup. This figure gives an example of the consistent differences in the spectrarecorded by the two sample presentation methods. Those recorded by thespinning cup had higher maximal absorbances, and greater amplitude of the peaksabove 1400 nm than did the standard transport. Both spectra are dominated byH2O, the strongest absorber of light energy in the NIR region; the peaks at 1400and 1900 nm are due to H2O. The spectra above 1400 nm, recorded using thestandard sample transport, tend to become “saturated” with the H2O signal (Fig. 5


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ThirdSecond

1.2

1.4

1.6

1.8

2.0

2.2

2.4

Abs

orba

nce,

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400 900 1400 1900 2400Wavelength, nm

First

Third Second

Figure 5. Transflectance spectra of thick manure samples recorded using Upper)the standard sample transport, and Lower) the spinning cup module. The order inwhich the scans were recorded is designated as “first” to “third”. Absorbance isexpressed as the raw optical data, log 1/R where R is reflectance, recorded by theNIR instrument. The discontinuity in the spectra at 1100 nm is due to changefrom one detector to another. The sample is not the same in the two panels.


Upper). Absorbance in the visible range, 400 to 700 nm is due to the colour inthe manure samples. There is a discontinuity at 1100 nm where there is a changefrom one detector to another in the instrument.

Thus, NIR spectra, expressed as log 1/R, are rather featureless. Except forthe general influence of water, little information is directly obtainable byinspection of the spectra. As indicated above, the spectra are rich in informationabout the samples, but the information is extracted statistically. Several types ofdata pre-treatment, such as smoothing to reduce the influence of noise, andderivatization to enhance the peaks and valleys in the spectra, may be required.

In the majority of cases, the replicate spectra for a sample were virtuallyidentical. But in the case of the some of the thicker samples, the spectra recordedin the standard transport showed decreasing absorbances (Fig. 5 upper). This wasattributed to settling of particulates onto the lower glass face of the sample cellover the few minutes required for the scanning. This could have changed theamount of diffuse reflectance such that less light was absorbed by the more tightlypacked layer of particles than by suspended particles. Because of this apparentsettling during scanning, it was decided to re-scan all the samples using a spinningcup module and to compare the success of calibration with the two methods.

For many samples, the replicate spectra recorded using the spinning cupwere almost identical, but some samples exhibited the pattern shown in Fig. 5lower of increasing absorbance from the first to the third scan of the same sample. It is not intuitively obvious why the spinning cup should have produced thispattern.

Principal Component Analysis of Spectra

Principal component analysis of the spectral data for the 75 samples in thisstudy showed that the first PC explained 90.44 % of the variance, the 2nd

explained an additional 7 % (cumulative 97.19 %), the 3rd, 2 % (cumulative 99.27%) and the 4th, 0.4 % (cumulative 99.63 %). Six PCs explained 99.9 % of thevariance.

Figure 6 is a two-dimensional scatter plot of scores for the first two PCs. It can be seen that there are two or three groups of spectra. One is a diagonal arrayon the left side of the plot. On the right side of the plot there may be two groups,one on each side of a “V”. Figure 7 is a plot of wavelength (x-variables) againstthe first PC that explains most of the variance in the spectral data. Strong positiveor negative loading means a wavelength is important in the variance in the spectraldata. Wavelengths from 500 to 700 nm are very strong (Fig. 7) and are associatedwith colour. Colour is not generally a useful property for predicting the quality ofsamples, except perhaps for chlorophyll. The strong positive wavelengths from


Fig. 6. Score plot for the spectra of the 75 manure samples in this study for thefirst two principal components

Fig 7. Relationships between PC1 representing 90% of the spectral variance inthe 75 manure samples in this study and wavelength from 400 to 2500 nm.


800 to 1300 nm and the negative wavelengths from 1400 to 2500 nm could beuseful areas for developing calibrations (Fig. 7).

Principal component analysis is a tool for comparing spectra of manuresamples from various types of hog operations, hog feeding regimes, types ofmanure storage (ponds vs slurry stores), types of manure treatment, periods ofstorage of samples, and so on. It will be useful for the selection of samples forthe development of robust calibrations for hog manure as it is in other NIRapplications.

Prediction of constituents in manure by NIRS

Table 6 shows the results of NIR calibrations for the physical parameters,nutrients, salts, metals, and minor elements in the 75 manure samples in thisstudy. The calibrations from the two sample presentation methods are presentedside by side.

The calibrations developed for % moisture, that ranged from 88.4 to 100 %water, were excellent (r2 >0.95) by both the standard sample transport andspinning cup methods. Conductivity was predicted less well than % moisture byboth methods (r2 ~ 0.7) (Table 6) and less well than in the Malley and Currie(1999) study (r2 = 0.85).

NIR results for NH4-N (r2 <0.8) (Table 6; Fig 8) were acceptable butconsiderably less successful than they were in the previous study (r2 = 0.97). Thereference analytical data agreed well between NWL and FWI, strong evidence thatthey are reliable. The range of NH4-N was greater in this study than in theprevious one, a situation that usually improves results. For other fractions of N, r2

were 0.77 to 0.83 (Fig. 8), compared with 0.94 to 0.98 in the previous study.

Fractions of P were predicted slightly more successfully (r2 0.77 to 0.91)than were N (Table 6; Fig. 8), but again less well than in the previous study (r2 =0.93 to 0.98).

None of the calibrations for K, Na, or Cl in this study were successful (r2 =0.19 to 0.54). In the previous study results were excellent for Na (r2 ~0.95) andmixed for K (r2 = 0.87, 0.66). The results in the present study were good for Ca (r2

~0.85) but were mixed in the previous study (r2 = 0.92, 0.65). NIR successfullypredicted Mg (r2 = 0.92) using the salts data but did less well with the ICP data (r2

~0.8). Excellent predictions for Mg were obtained in the previous study (r2 =0.98).

Suspended C was predicted with r2 = 0.77 here and with r2 = 0.98 in theprevious study.

The results for the metals and minor elements can be grouped into three


classes. Good calibrations with r2 ~>0.9 were achieved for Ba, Cd, Cu (standard),and standard). Acceptable calibrations with r2 from 0.75 to 0.9 were developedfor Be (standard), Cr, Fe, Mn, Mo, Ni, Se, Sr, S, Ti, V, and Zn (standard). A fewelements, Si, Co, and Pb were predicted with marginal success (r2 ~0.7) andpredictions were not successful for Al, As, B, and Li (Table 6).

Table 6. Accuracy of prediction for NIR calibrations for various constituents in 75 samples ofhog manure determined by the two sample presentation methods. “NWL” refers to analyses byNorwest Labs; “FWI”, to analyses by the Freshwater Institute. “Std” is the standard transportsample presentation. Units for SEP are given in Table 1 and Appendix A.

Statistic % Moisture Conductivity NH4-N NH4-N

NWL NWL NWL FWI

Std Spin Std Spin Std Spin Std Spin

r2 0.94 0.97 0.68 0.72 0.69 0.79 0.79 0.55

SEP 0.69 0.44 2.49 2.37 0.44 0.37 0.37 0.94

RPD 4.11 5.84 1.76 1.90 1.80 2.18 2.20 1.49

RER 16.35 21.74 7.62 7.80 6.57 8.25 9.11 8.68

Statistic TDN Susp N Total N Total N

FWI FWI NWL FWI


r2 0.83 0.86 0.77 0.83 0.86 0.77

SEP 0.52 0.52 0.62 0.52 0.52 0.62

RPD 2.44 2.64 2.11 2.44 2.64 2.11

RER 8.99 10.77 8.66 8.99 10.77 8.66


Statistic TDP SRP Susp P Total P

FWI FWI FWI FWI


r2 0.91 0.65 0.93 0.68 0.79 0.74 0.86

SEP 0.15 0.47 0.13 0.45 0.18 0.34 0.29

RPD 3.39 1.70 3.87 1.77 2.18 1.95 2.73

RER 11.72 9.58 13.40 9.92 8.45 9.94 9.24

Statistic Total P - nutr P - ICP Susp C K - nutr

NWL NWL FWI NWL


r2 0.77 0.91 0.84 0.84 0.77 0.54 0.43

SEP 0.41 0.32 0.41 0.52 3.46 0.32 0.42

RPD 2.08 3.33 2.51 2.48 2.07 1.47 1.33

RER 8.49 17.12 9.28 11.89 7.82 4.22 6.95

Statistic K - ICP Na-nutr Na -salts Na -ICP

NWL NWL NWL NWL


r2 0.40 0.52 0.30 0.25 0.32 0.21 0.33 0.19

SEP 0.38 0.35 0.16 0.18 0.18 0.20 0.21 0.25

RPD 1.28 1.45 1.20 1.15 1.22 1.13 1.22 1.11

RER 4.64 5.21 4.75 5.06 5.71 5.18 5.87 5.12


Statistic Ca - salts Ca - ICP Mg -salts Mg - ICP

NWL NWL NWL NWL


r2 0.83 0.87 0.85 0.83 0.92 0.93 0.82 0.79

SEP 0.47 0.45 0.40 0.47 0.21 0.21 0.26 0.34

RPD 2.41 2.76 2.62 2.44 3.63 3.70 2.33 2.15

RER 9.28 11.81 11.10 9.47 17.00 17.42 9.83 10.84

Statistic Cl Al As Ba

NWL NWL NWL NWL


r2 0.46 0.47 0.58 0.65 0.57 0.63 0.89 0.91

SEP 0.33 0.33

RPD 1.36 1.37 1.54 1.43 1.52 1.64 2.97 3.32

RER 5.35 5.29 6.48 8.63 5.69 6.40 10.73 15.21

Statistic Be B Cd Cr

NWL NWL NWL NWL


r2 0.93 0.65 0.68 0.58 0.90 0.90 0.88 0.87

RPD 3.77 1.69 1.75 1.55 3.10 3.25 2.92 2.74

RER 13.46 5.96 7.36 6.78 11.44 13.52 11.78 11.14


Statistic Co Cu Fe Pb

NWL NWL NWL NWL


r2 0.51 0.79 0.93 0.81 0.84 0.84 0.73 0.70

RPD 1.42 2.18 3.87 2.30 2.48 2.49 1.93 1.82

RER 6.53 11.07 16.82 10.43 8.82 12.70 6.72 6.44

Statistic Li Mn Mo Ni

NWL NWL NWL NWL


r2 0.54 0.49 0.83 0.87 0.84 0.81 0.84 0.73

RPD 1.48 1.40 2.40 2.72 2.49 2.28 2.48 1.94

RER 5.92 7.35 10.08 11.26 9.27 11.0 11.55 9.02

Statistic Se Si Sr S

NWL NWL NWL NWL


r2 0.80 0.83 0.67 0.70 0.83 0.72 0.86 0.88

RPD 2.18 2.40 1.75 1.82 2.43 1.89 2.63 2.86

RER 8.94 10.03 7.45 8.88 10.40 8.99 9.79 10.50


Statistic Ti V Sn Zn

NWL NWL NWL NWL


r2 0.84 0.78 0.84 0.83 0.92 0.82 0.80 0.55

RPD 2.50 2.12 2.48 2.43 3.52 2.39 2.25 1.50

RER 9.11 10.14 7.33 9.65 11.71 11.19 9.73 7.89

Despite lower performance than in the previous study, the agreement between NIR-predicted and reference analytical concentrations should be viewed in the context of theperformance of the chemical analytical methods. The chemical analytical results were subject toa very few extreme outliers (not shown) and some less easily-detected outliers (see Table 3 for Kand Na). The agreement achieved among the methods and labs for the same constituent, afterremoval of obvious outliers, is in the region of r2 = 0.82 to 0.93. Many of the NIR calibrationsin this study performed to this level (Table 6).

Use of the spinning cup did not generally improve the success of the calibrations eventhough it ensured that the thick slurries would not settle during scanning. The spectral results inFig. 5 indicate the importance of sample management during scanning in the laboratory. Nevertheless, settling of particles during scanning will not be a concern if NIR fibre optic probesare inserted into a high pressure manure stream in the field. Furthermore, new NIR instrumentson the market tend to use technology such as diode arrays that record spectra in milliseconds.


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Figure 8. Linear regression relationships between the NIR-predicted and the chemically-determined values for several constituents in manure. The line is 1:1 and goes through theorigin. Upper left) NH4-N using FWI values; Upper right) Total N using NWL nutrient values;Middle left) Total P using NWL ICP values; Middle right) Total P using NWL nutrient values;Lower left) Total P using FWI values


Discussion This study expanded on the results previously reported by Malley and Currie (1999) in

three ways. It repeated the study of the feasibility of developing useful NIR calibrations fornutrients, N, P, and K in hog manure. It expanded the sources of variability in the sample setfrom 7 to 25 ponds. It greatly expanded the constituents examined from physical parameters,nutrients and salts to include 27 metals and minor elements.

The NIR results achieved here were useful for NH4-N, TDN, Suspended N, Total N,TDP, SRP, Suspended P, Total P, Suspended C, Ca, Mg, Ba, Be Cd, Cu, Cr, Cu, Fe, Mn, Mo,Ni, Se, Sr, S, Ti, V, and Zn. Useful results were not obtained for Na, K, Cl ,Al, As, B, and Li.

A potential confounding factor in this study was the fact that the samples were notfreshly-collected but had been stored for up to six months before the NIR scanning. To attemptto offset changes in chemical speciation that may have occurred with storage, the nutrients, Nand P, were re-analyzed close to the time of NIR scanning.

The NIR predictive results achieved in this study were uniformly less successful, i.e,judged by the magnitude of the coefficient of determination, r2, between the chemical referencevalues and the NIR-predicted values, than they were in the previous study. The explanation maylie in the fact that the samples here were stored whereas they were freshly-collected in theprevious study. The results from the re-analysis of N and P do not support the hypothesis thatthe samples changed with storage in a major way. NH4-N concentrations in the samplesdetermined by NWL and FWI at times separated by a few months agreed well. The results didnot indicate that there was a major loss of gaseous NH3 from the samples over time as the bottleswere repeatedly opened for subsampling. Finally, the NIR results achieved for the nutrients withthe FWI reference analytical data were not superior to those achieved with the NWL data, eventhough they were obtained close to the time of scanning.

Alternatively, the reference chemical data may have been of poorer quality here than inthe previous study. The analyses in the FWI for C, N, and P fractions were performed using thesame methods and by the same technicians for the two studies. There was generally goodagreement between the values obtained for constituents by NWL and FWI, arguing againstproblems with the analytical data.

The explanation for the difference between the two studies is most likely that there isgreater variability among the samples from 25 ponds in this study compared with 7 in theprevious study. The previous study had the required range of constituent concentrations, butincluded in the sample set multiple samples from the same ponds. The present study had a morebalanced design with 3 samples from each of 25 ponds. An inverse relationship between thestatistical success of NIR calibrations and the amount of variability in the sample set has beenfound in other media, such as soil, where the best NIR results were achieved in a set of 28 soilsamples, and the least good in a set of 1000 soil samples (Malley et al., in press).

Discussion of the spectral basis for NIR prediction of the parameters in this study isbeyond the scope of this report. The literature reports that NH4 ion absorbs in the near-infrared


region. The field of NIR is based on the absorption of light by organic molecules containing C-O, C-H, C-N. Absorption of P-containing compounds is well known. Water is the strongestabsorber in the NIR region and is widely used for monitoring moisture in materials. The basisfor predicting pH and conductivity is not clear. The basis for predicting metals and otherelements can lie in their correlation with organic matter (they are indirectly predicted); theircorrelation with clay (especially in soils); their effect on water (for liquids); or they may bespectrally-active. Distinguishing among these possibilities requires further research.

The use of NIRS for the rapid analysis of hog manure appears to be an originalapplication (Bibliography of the International Committee for Near-Infrared Spectroscopy withover 10,000 entries). NIRS has been used for the determination C, N and ash in cattle manure(Asai et al., 1993; Kinoshita et al., 1997) and for % moisture, NH3, K, P, pH, C and N in dairymanure (Reeves and Vankeseel, under review). Reeves and Vankeseel achieved excellent resultsfor % moisture, NH3, C, and N but were unable to predict P. K and pH were predicted withmarginal success. This agrees with our lack of success for K but disagrees strongly for P, thathas been one of the best predicted constituents in both hog manure studies.

For NIR to operate commercially for manure analysis in the field, calibrations will haveto be reliable and robust (capable of dealing with variability in the samples). This study showsthat NIRS potentially can be used to monitor rapidly the important nutrients, NH4-N, total N, P,and numerous metals and minor elements. The next steps to developing commercially-viablecalibrations will include further examination of the overall variability of hog manure and thepossibility that calibrations will best be tailored to categories of manure samples. Within thescope of a calibration, samples will be selected using available software such as Unscrambler orInfrasoft International so that they uniformly represent the range of variability. Highly preciseand accurate analytical reference data must be obtained on the samples, such as by analyzingeach sample in duplicate or triplicate and using mean values. Samples that are outliers due toanalytical error or spectral inconsistencies will be identified and removed using the sophisticatedsoftware. Finally, calibrations must be monitored by comparing predicted values with chemicalanalytical values. Samples that are not predicted well by a calibration may be outside the rangeof variability included in the calibration. The samples are added into the calibration set and anew, updated calibration is developed. Experience in grain protein monitoring shows that thecalibration becomes increasingly robust, less chemical monitoring is required, and updating lessfrequent over a period of several years.


References Asai, T., S. Shimizu, et al. 1993. Quick determination of total nitrogen, total carbon, and crudeash in cattle manure using near infrared reflectance spectroscopy. Nippon Dojo Hiryogaku Zasshi64(6): 669-675.

Brookes, I.K., B.N. Gedge, and S.V. Hammon. 1996. Applications of near infraredspectroscopy to fermentation process analysis. pp 259-271. In A.M.C. Davies and PhilWilliams (Eds). Near Infrared Spectroscopy: The Future Waves. NIR Publications, ChichesterU.K.

Espinoza, L.H., D. Lucas, and D. Littlejohn. 1999. Characterization of hazardous aqueoussamples by near-IR spectroscopy. Applied Spectroscopy 53: 97-102.

Gatin, M.R., J.R. Long, J.R., P.W. Schmitt, P.J. Galley and J.F Price. 1996. Comparison ofclinical studies: near infrared predictions of multiple analytes in human sera. pp 347- 352. InA.M.C. Davies and Phil Williams (Eds). Near Infrared Spectroscopy: The Future Waves. NIRPublications, Chichester U.K.

Hall, J.W., B. McNeil, M. J. Rollins, I. Draper, B.G. Thompson, and G. Macaloney. 1996. Near-infrared spectroscopic determination of acetate, ammonium, biomass, and glycerol in anindustrial Escherichia coli fermentation. Applied Spectroscopy 50: 102-108.

Kinoshita, T., A. Ichikawa, et al. 1997. The easy determination of a few chemical properties oflivestock manure compost by near-infrared spectroscopy.” Aichi-ken Nogyo Sogo ShikenjoKenkyu Hokoku 29: 305.

Malley, D.F. and R.S. Currie. 1999. Feasibility study on the rapid analysis of available N and Pin hog manure and manure-amended soils by near-infrared spectroscopy. Report to the ManitobaLivestock Manure Management Initiative Inc. and The Prairie Farm RehabilitationAdministration. March, 36 pp.

Malley, D.F. and P.C. Williams. 1997. Use of near-infrared reflectance spectroscopy inprediction of heavy metals in freshwater sediment by their association with organic matter.Environmental Science and Technology 31: 3461-3467.

Malley, D.F., L. Yesmin, R.G. Eilers, P. Haluschak, P.D. Martin, and L. M. McClintock.Feasibility of analyzing archived Canadian Prairie agricultural soils by near-infrared reflectancespectroscopy. Proceedings of the 9th International Conference on Near-infrared Spectroscopy,Verona, Italy, 13-18 June 1999 (in press)

Racz, G., M. Fitzgerald, G. Plohman, and J. Hicks. Long-term effects of hog manure on soilquality and productivity. Report to Agricultural Research Development Initiative on Project 98-124 (in preparation).

Reeves, J.B.,III and J.S. Vankessel. Near-infared spectrscopic determination of carbon, total


nitrogen, and ammonia in dairy manures. J. Dairy Science (under review).

Stainton, M.P., M..J. Capel, and F.A.J. Armstrong. 1977. 2nd ed. The chemical analysis of freshwater. Fisheries and Environment Canada, Fisheries and Marine Service, Miscellaneous SpecialPublication No. 25, 180 pp.

Wust, E, A. Fehrman, A. Hoffman and L. Rubzik. 1996. In-line measurement of high moistureproducts. pp 268-271. In A.M.C. Davies and Phil Williams (Eds). Near Infrared Spectroscopy:The Future Waves. NIR Publications, Chichester U.K


AppendixAppendix A. Chemical analytical data supplied on the 75 samples of hog manure in this studyby Norwest Labs. Samples with concentrations less than the limits of detection are omitted.

Parameter Symbol Numberof

samples

Value

Mean SD Min Max

moisture, % 741 97.23 2.7 88.4 100

electricalconductivity, mS/cm

EC 75 15.24 4.36 9.01 27.5

ammonium-nitrogen,g/L

NH4-N 75 2 0.79 0.82 3.68

total nitrogen, g/L Total N 75 2.77 1.32 0.9 6.5

total phosphorus, g/L Total P- nutr 75 0.87 0.96 0.05 5.51

phosphorus, g/L P - ICP 75 1.04 1.16 0.058 6.19

potassium, g/L K - nutr 75 1.35 0.52 0.26 3.5

potassium, g/L K - ICP 75 1.48 0.5 0.68 2.6

sodium , g/L Na - nutr 75 0.42 0.2 0.15 1.07

sodium , g/L Na - salts 75 0.45 0.22. 0.2 1.23

sodium, mg/L Na - ICP 75 0.51 0.26 0.18 1.5

calcium, g/L Ca - salts 75 1.18 1.18 0.07 5.37

calcium, g/L Ca - ICP 75 1.07 1.09 0.06 4.51

magnesium, g/L Mg - salts 75 0.65 0.72 0.02 3.59

magnesium, g/L Mg - ICP 75 0.58 0.67 0.01 3.7

chloride, g/L Cl 75 1.11 0.45 0.42 2.22

1 One extreme outlier was removed

feasibility study on the rapid compositional … study on the rapid compositional analysis of hog...

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