ChemWiz Analyzer Development Kit
Full spectrum chemometric analysis for multiple components can now be performed using the ChemWiz® Analyzer Development Kit (ADK). This software toolkit, when combined with StellarNet spectrometer instrumentation and SpectraWiz operating software, enables rapid development of complex analyzers needed to provide custom measurement solutions for many applications.ChemWiz Analyzer Development Kit
& Wizard Runtime Engine
StellarNet Spectrometer Hardware
NIR Spectrometers (900-2500nm)
Light Sources, Fibers, Accessories
Applications
Plastics &Polymers
Pharmaceutical & Supplement
Chemical & Industrial
Full spectrum chemometric analysis for multiple components can now be performed using the ChemWiz® Analyzer Development Kit (ADK). This software toolkit, when combined with StellarNet spectrometer instrumentation and SpectraWiz operating software, enables rapid development of complex analyzers needed to provide custom measurement solutions for many applications.
Real-time spectral data is used as input to measure concentrations of up to 6 components such as moisture, fat, protein, alcohols, acids, or chemicals such as pesticides. Alternatively, the ChemWiz® ADK can be used to classify samples, such as the types of plastic.
The toolkit includes a model builder which is used to process spectra from a set of known component concentrations. Here the spectral pre-processing techniques are selected such as multivariate scatter correction (MSC), smoothing, derivatives, and mean centering. The multivariate analysis type is then selected such as partial least squares (PLS). Finally a cross validation technique is chosen and used to improve the reported statistics indicating accuracy and fitness of the calibration model created.
The ADK includes a runtime engine that performs the analysis in real-time with little to no sample preparation. The measurement results are displayed on the SpectraWiz Application panel for “Chemometric Predictor” which is also provided in our LabVIEW software program for OEM developers.
Each analyzer system can be tailored with sampling accessories to measure liquids, solids, and powders. For liquids, absorbance is measured via flow cells, dip probes, or cuvettes. For solids and powders, reflectance is typically used to measure sample absorbance. Near infrared diode array spectrometers such as the DWARF-Star NIR-512 with TEC cooling are typically configured for analyzer development.
ChemWiz® Chemometric predictor panel in SpectraWiz displaying component concentration values in real-time.
Flow Chart of Analyzer Development
2. Collect spectra of each Standard Sample using StellarNet Spectrometer Instrumentation.
3. Load collected Spectra into the chemometric model builder and select (a) desired wavelengths of calibration (b) spectral preprocessing (c) cross validation methods (d) and model of choice.
4. Once the calibration model is built it can be verified using statistics indicating accuracy and fitness of the model. The end user may build many different models for different sample types and matrices (i.e. different types of cheese powders may require separate models for the highest accuracy of prediction).
5. Load model file into SpectraWiz and open ChemWiz Chemometric predictor panel to view calculation of your parameters of choice from each collected spectrum in real-time.
ChemWiz ADK Features
- Multivariate algorithms for Quantitative analysis
– Partial Least Squares PLS, Principle Component Regression PCR - Discriminant methods for Qualitative analysis
– Principle Component Analysis PCA, and MDR - Preprocessing options to stabilize data
– Sample averaging, Spectral smoothing,
– First or second derivative with gap select,
– Multiplicative scatter correction, Mean centering,
– and lots more preprocessing options. - Visual graphs and Model cross validation
- Applications include:
– Food Safety
– Packaging / Process
– Beverage QC
– Agriculture Feed
– Pharmaceutical
– Oil & Chemical
– Plastics
Price List and Specifications
Item | Description | Price |
ChemWiz-ADK | Chemometric model builder | $3500* |
Wizard-runtime | Runtime engine for chemometric models – one license only | $1500* |
* Contact StellarNet application scientist as price will vary with choice of 3rd party vendor for model and predictor engine
Specifications | |||
Operating System | WinXP, Vista, Win 7 | Runtime speed | 3Hz max |
Runtime | SpectraWiz v5.0 or higher | Full Spectrum Measures | Concentrations or Classification |
Spectral range | 190 – 2500nm | Analysis (typical) | PLS or PCA/MDR |
StellarNet Spectrometer Instrumentation
StellarCASE-NIR
ChemWiz Analyzer Applications
Food and Agriculture
Forage & Feedstuffs
Alfalfa – Barley straw – Bromegrass – Corn – Timothy – Hays
1. Abrams, SM., J.S Shenk, and H.W. Harpster. 1988. Potential of near IR reflectance spectroscopy for analysis of silage composition. J. Dairy Sci. 71:1955-1959.
2. Agung, P., K. Mitsunori, N. Takehiro, T. Fuminori, A. Akira, and H. Tatsuo. 1997. Two methods of near infrared reflectance spectroscopy for digestibility and energy value feeds. Anim. Sci. Technol. 67:851-861
3. Pazdernik, D.L., A.S. Killam, and J.H. Orf. 1997. Analysis of amino and fatty acid composition in soybean seed. Agron. J. 89:679-685
Fruits and Vegetables
Sample |
Parameter |
Apple | acid11, bruised tissue, dry matter, firmness, fructose, glucose11, moisture12 , pH, solube solids, sugar12 |
Cherry | firmness13, pit detection, soluble solids |
Date | moisture, soluble solids |
Orange | citric acid, malic acid, fructose, glucose, purity |
Papaya | carotenoids, chlorophyll, maturity, soluble soids |
Potato | bruised tissue, discoloration, maturity, sorbitol, sucrose |
Tomato | acidity, maturity, soluble solids |
11. Budiastra, I.W., Y. Ikeda, and T. Nishizu. 1998. Optical Methods for quality evaluation of fruits. Part 2. Prediction of individual sugars and malic acid concentrations of apples and mangoes by the developed NIR reflectance system. J. Jpn. Soc. Agric. Machin.60:117-127
12. Murakami, M., J Himoto, and K. Itoh. 1994. Analysis of apple quality by near-infrared reflectance spectroscopy. J. Fac. Agric. Hokkaido Univ. Jpn. 66:51-61
13 Lu, R. 2001a. Predicting firmness and sugar content of sweet cherries using near-infrared diffuse reflectance spectroscopy. Trans. ASAE 44:1265-1271
Small Grain Crops
Barley – Rice7 – Wheat
4. American Society of Brewing Chemists. 1998. Report of subcommittee on protein and moisture in whole-grain barley by near infrared spectroscopy. J. Am. Soc. Brew. Chem. 56:189-194.
5. Delwiche, S.R. and W.R. Hruschka. 2000. Protein content of bulk wheat from near infrared reflectance of individual kernels. Cereal Chem. 77:86-88.
7. Iwamoto, M., T. Suzuki, N. Kongseree, J. Uozumi, and O. Inatsu. 1986. Analysis of protein and amino acid contents in rice flour by near-infrared spectroscopy. Nippon Shokuhin kogyo Gakkaishi 33:848-853.
Beverages and Brewing
14. Buchanan, B.R., D.E. Honigs, C.J. Lee, and Roth. 1988. Detection of ethanol in wines using optical-fiber measurements and near infrared analysis. Appl. Spectrosc.42(6):1106-1111.
15. Coventry, A.G., and M.J. Hunston. 1984. Applications of near-infrared spectroscopy to the analysis of beer samples. Cereal Foods World 29:715, 717-718.
Meats
Pork – Chicken – Turkey – Beef – Lamb – Fish – Ground Meats – Frozen
14. Hildrum, K.I., B.N. Nilsen, M. Mielnik, and T. Naes. 1994. Prediction of sensory characteristics of beef by near infrared spectroscopy. Meat sci. 38:67-80.
15. Windham, W.R., and W.H. Morrison. 1998. Prediction of fatty acid content in beef neck lean by near infrared reflectance analysis. J. Near Infrared Spectrosc. 6:229-234.
Dairy and Eggs Products
Milk – Milk Powder – Cheese – Cheese Powders – Butter – Whey
16. Barabassy, S., and K. Kaffka. 1993. The application possibilities of the near infrared technique in the non destructive investigation of mixed milk powder products. J. Food Phys. 57:39-48.
17. Frank, J.F. and G.S. Birth. 1982. Application of near infrared reflectance spectroscopy to cheese analysis. J. Dairy Sci. 65:1110-1116.
Coffee, Tea, Tobacco, & Related Products
Commercial coffee is mainly made up of two main blends, Arabica and Robusta. Main distinguishing absorptions can be found from water content (1466 and 1962nm) and lipids (1209, 2308, and 2346nm)8. NIRS can be used for classification as well as blending. Also, caffeine and dry matter content can be determined. Green9, black, and oolong tea can be analyzed for different different polyphenols, caffeine, and amino acids by using various wavelengths in the NIR thus allowing rapid determination of factors relating to tea taste and health parameters. Since the 70’s NIRS has been used to predict the total reducing sugar content in tobacco and total alkaloids providing valuable information about the mildness and aroma of the smoke10.
8. Downey, G., and J. Boussion. 1996. Authentication of coffee bean variety by near-infrared reflectance spectroscopy of dried extract. J. Sci. Food Agric. 71:41-49.
9. Ikegaya, K.,1990. Determination of chemical constituents in processed green tea by near infrared analysis. JARQ 24:49-53.
10. Hamid, A., W.F. McClure, and W.W. Weeks. 1978. Rapid spectrophotometric analysis of chemical composition of tobacco. Part 2. Total alkaloids. Beitr. Tabakforsch. Int. 9:267-274.
Fats and Oils
Sample |
Parameter |
Soybean oil | acid values, iodine, cis/trans FAs |
Olive oil18 | Free Fatty Acids, total polyphenols, K270, 235, 225 , moisture, a |
Sunflower | linoleic, palmitic, palmitoleic, stearic, & oleic acids |
Sesame oil | linoleic, palmitic, palmitoleic, stearic, & oleic acids |
Butter | moisture, fat, NaCl, nonfat solids, purity |
18. Garrido, A., C. Cobo, J. Garicia-Olmo, M.T. Sanchez-Pineda, R. Alcala, J.M. Horcas, and A. Jimenez. 2000. The feasibility of near infrared spectroscopy for olive oil quality control. p. 867-871. InA.M.C. Davies and R. Giangiacomo (ed.) Near Infrared spectroscopy: Proceedings of the 9th Int. Conference. NIR publications, Chichester, UK.
19. Perez-Vich, B., L Velasco, and J.M. Fernandez-Martinez. 1998. Determination of seed oil content and fatty acid composition in sunflower through the analysis of intact seeds, husked seeds, meal, and oil by near infrared reflectance spectroscopy. J. Am. Oil Chem. Soc. 75:547-555.
Polymers and Plastics Production
Polymers and Plastics ProductionTechnical Information Request
Investigating the Effects of Full-Spectrum LED Lighting on Strawberry Traits Using Correlation Analysis and Time-Series Prediction
Yuze Lu, Mali Gong, Jing Li, and Jianshe Ma Published: 5 January 2024AbstractIn crop cultivation, particularly in controlled environmental agriculture, light quality is one of the most critical factors affecting crop growth and harvest. Many scholars have studied...