Use of Near-Infrared Spectroscopy to Predict Potential Mushroom (<i>Agaricus Bisporus</i>) Yield of Phase II Compost

Sharma, H. S. S.; Kilpatrick, M.

doi:10.1366/0003702001948367

Cited by 8 publications

(15 citation statements)

References 6 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A PLS model (r 2 =0.90) based on the ten parameters was constructed for predicting yield with a SEP of 15.5kg tonne -1 . 10 In addition, Sharma, and Kilpatrick 9 have also examined differences in the yield of the composts and their relationship to NIR spectra. A calibration equation (r 2 =0.93) using the spectral data between 1100 and 2498nm obtained from the samples of the four trials was developed for predicting fresh mushroom yield with a SEP of 18.6 kg tonne -1 .…”

Section: Resultsmentioning

confidence: 99%

“…A calibration equation (r 2 =0.93) using the spectral data between 1100 and 2498nm obtained from the samples of the four trials was developed for predicting fresh mushroom yield with a SEP of 18.6 kg tonne -1 . 9 The repeatability of the reference test is the most important factor affecting the performance of the NIR regression equation to predict the target values. This was carried out by analysing selected samples in triplicate for variance between the measurements and statistical significance of the parameters measured in any of the calibration and validation samples was not observed.…”

Section: Resultsmentioning

confidence: 99%

“…With extensive sample selection 7 and robust calibrations, 8 this technique should be able to predict a wide range of compost characteristics including potential mushroom yield. 9 For predictive purposes, a set of samples with known chemical composition is required as a calibration set. Each sample will produce its own characteristic NIR spectra with specific wavelength relating to different chemical components of the sample.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determination of Phase II Mushroom (Agaricus Bisporus) Compost Quality Parameters by near Infrared Spectroscopy

Sharma¹,

Kilpatrick²,

Burns³

2000

Journal of Near Infrared Spectroscopy

Self Cite

View full text Add to dashboard Cite

The microbial and biochemical characteristics of compost prepared from wheat straw and chicken litter can determine the total yield of fruiting bodies of Agaricus bisporus. With the aim of identifying key substrate parameters and their relationship with mushroom yield, compost samples were obtained from four comparative trials carried out over a seven year period. The objective of this study was to assess the use of near infrared (NIR) reflectance spectroscopy for determining important characteristics of pasteurised compost. The samples were analysed for moisture content, microbial population, pH, conductivity, nitrogen, carbon and hydrogen, nitrogen, ash, ammonia, Na, Cu, Fe, acid detergent fibre, neutral detergent fibre, lignin and fibre fractions as determined by thermal analysis. Results from conventional analytical methods were correlated with NIR data and partial least squares regression analysis used to assess the information in the absorbance spectrum, which determines the concentration of the key parameters. Visible and NIR scans of the calibration samples made over the range of 400-2498 nm indicated that best calibrations could be developed using the 1100-2498 nm segment. NIR calibration equations for predicting NDM, ash, carbon, thermophilic population and fibre fractions have been successfully developed and then validated using independent samples.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Phase II Mushroom (Agaricus Bisporus) Compost Quality Parameters by near Infrared Spectroscopy

Sharma¹,

Kilpatrick²,

Burns³

2000

Journal of Near Infrared Spectroscopy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Microbial biomass, basal respiration rates and enzyme activities, for example, protease activity or the hydrolysis of fluorescein diacetate which is indicative of total enzymatic activity, were successfully or satisfactorily predicted in 42 samples of co-composted green wastes and sewage sludges and in a set of 97 yard waste composts. 24,25 Sharma and Kilpatrick 26 reliably assessed the potential yield of mushrooms for phase II compost. to our knowledge, only one attempt was made to predict the results of bioassays.…”

Section: Introductionmentioning

confidence: 99%

Predictive Capacity of Visible-Near Infrared Spectroscopy for Quality Parameter Assessment of Compost

Herrman

Mayer

Michel

et al. 2009

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Screening tests are basic procedures commonly used to assess compost quality. Important parameters for quality assessment are the germination capacity and the suppression of plant pathogens which have to be measured by time-consuming laboratory methods. The objective was to test whether visible (vis) and near infrared (NIR) spectroscopy (vis-NIR) is useful to analyse parameters important for compost quality. Ninety seven compost samples from Switzerland were analysed by conventional methods and by vis-NIR. The content of organic (Corg) and inorganic C (Cinorg), total N (Ntot), mineralisable N after 56 days (Nmin_d56), total P (Ptot), K, Ca and salt, the C/N ratio, pH and microbiological characteristics [hydrolysis of fluorescein diacetate (FDA-hydrolysis) as indicator of total enzyme activity and cellulase activity] were determined. Furthermore, plant tolerance and the suppression of pathogens were tested using germination tests with salad, cress, ryegrass and bean or a Rhizoctonia solani bioassay, respectively. The samples were scanned in the range of 400–2500 nm (visible light and NIR) using a Foss NIRSystems spectrometer 6500. A modified partial least squares regression method and the whole spectrum were used to develop cross-validation equations for all constituents. For this, the first to third derivative was calculated. The prediction accuracy was evaluated as excellent for Corg and good for N, and the C/N ratio based on the RSC values (ratio of standard deviation of laboratory results to standard error of cross-validation) and the coefficients of determination ( r2). Approximate quantitative predictions were possible for the contents of Ptot, K, Ca and salt, whereas for the constituents Cinorg, Nmin_d56, FDA-hydrolysis and the germination tests with cress and salad only between high and low values could be discriminated. Unsuccessful predictions as indicated by RSC values lower than 1.5 and r2 values below 0.50 were obtained for pH, cellulase activity, germination tests with ryegrass and bean and the disease suppression test using R. solani. Overall the results of the present study indicate that vis-NIR spectroscopy has the potential to be used for quality assessment of composts and to replace time-consuming methods such as germination tests using salad and cress. However, the use for monitoring purposes requires further research to clarify whether other complex quality parameters such as disease suppression indicators may also be predicted successfully.

show abstract

“…5 Near-infrared spectroscopy (NIRS) has been widely used for quality assessment of agricultural produce such as forages, feeds, and mushroom compost. [6][7][8] The application of spectroscopy for the evaluation of flax started in the 1990s to determine the degree of retting using NIRS and mid-infrared (MIR) 9 spectroscopy and characterization of flax by MIR and Raman spectroscopy to determine changes during retting. 10 Archibald and Akin 11 developed a model for predicting Fried's degree of flax retting scores using selected NIR wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Visible and Near-Infrared Spectroscopy as a Tool for Assessing Fiber Fineness during Mechanical Preparation of Dew-Retted Flax

Sharma

Reinard

2004

Appl Spectrosc

Self Cite

View full text Add to dashboard Cite

Flax fiber must be mechanically prepared to improve fineness and homogeneity of the sliver before chemical processing and wet-spinning. The changes in fiber characteristics are monitored by an airflow method, which is labor intensive and requires 90 minutes to process one sample. This investigation was carried out to develop robust visible and near-infrared calibrations that can be used as a rapid tool for quality assessment of input fibers and changes in fineness at the doubling (blending), first, second, third, and fourth drawing frames, and at the roving stage. The partial least squares (PLS) and principal component regression (PCR) methods were employed to generate models from different segments of the spectra (400-1100, 1100-1700, 1100-2498, 1700-2498, and 400-2498 nm) and a calibration set consisting of 462 samples obtained from the six processing stages. The calibrations were successfully validated with an independent set of 97 samples, and standard errors of prediction of 2.32 and 2.62 dtex were achieved with the best PLS (400-2498 nm) and PCR (1100-2498 nm) models, respectively. An optimized PLS model of the visible-near-infrared (vis-NIR) spectra explained 97% of the variation (R(2) = 0.97) in the sample set with a standard error of calibration (SEC) of 2.45 dtex and a standard error of cross-validation (SECV) of 2.51 dtex R(2) = 0.96). The mean error of the reference airflow method was 1.56 dtex, which is more accurate than the NIR calibration. The improvement in fiber fineness of the validation set obtained from the six production lines was predicted with an error range of -6.47 to +7.19 dtex for input fibers, -1.44 to +5.77 dtex for blended fibers at the doubling, and -4.72 to +3.59 dtex at the drawing frame stages. This level of precision is adequate for wet-spinners to monitor fiber fineness of input fibers and during the preparation of fibers. The advantage of visNIR spectroscopy is the potential capability of the technique to assess fineness and other important quality characteristics of a fiber sample simultaneously in less than 30 minutes; the disadvantages are the expensive instrumentation and the expertise required for operating the instrument compared to the reference method. These factors need to be considered by the industry before installing an off-line NIR system for predicting quality parameters of input materials and changes in fiber characteristics during mechanical processing.

show abstract

Use of Near-Infrared Spectroscopy to Predict Potential Mushroom (Agaricus Bisporus) Yield of Phase II Compost

Cited by 8 publications

References 6 publications

Determination of Phase II Mushroom (Agaricus Bisporus) Compost Quality Parameters by near Infrared Spectroscopy

Determination of Phase II Mushroom (Agaricus Bisporus) Compost Quality Parameters by near Infrared Spectroscopy

Predictive Capacity of Visible-Near Infrared Spectroscopy for Quality Parameter Assessment of Compost

Evaluation of Visible and Near-Infrared Spectroscopy as a Tool for Assessing Fiber Fineness during Mechanical Preparation of Dew-Retted Flax

Contact Info

Product

Resources

About