As the number falls, alternatives to the Hagberg-Perten falling number method: A review.

This review examines the application, limitations, and potential alternatives to the Hagberg-Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.

Detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system.

The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000-1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments.

Measurement of inorganic phosphorus in soybeans with near-infrared spectroscopy.

This study explored the feasibility of near-infrared (NIR) quantitative and qualitative models for soybean inorganic phosphorus (Pi), which is complementary to phytic acid, a component of nutritional and environmental importance. Spectra, consisting of diffuse reflectance (1100-2500 nm) of ground meal and single-bean transmittance (600-1900 nm) of whole seed, were collected on 191 recombinant inbred soybean lines. Partial least-squares regression models were individually developed for soy meal diffuse reflectance, single-bean transmittance, and averaged (24 beans/line) whole seed transmittance data. The best performance was obtained with diffuse reflectance data, in which the standard errors (rmsd) were 263 and 248 mg/kg for cross-validation and validation sets, respectively. Model accuracy was lower for the 24-bean average transmittance spectra and still lower for single beans. Despite the overall poorer modeling ability of Pi with respect to the common macronutrient NIR regressions, such as those for protein and oil, this technique holds promise for use in breeding programs.

Binary mixtures of waxy wheat and conventional wheat as measured by NIR reflectance.

Waxy wheat contains very low concentration (generally <2%) of amylose in endosperm starch, in contrast to conventional wheat whose starch is typically 20% amylose, with the balance being the branched macromolecule, amylopectin. With the release of a commercial hard winter waxy wheat cultivar in the United States, the grain trade, milling, and processing industries seek to have a rapid technique to ensure the purity of identity preserved waxy wheat lots. Near infrared (NIR) reflectance spectroscopy, a technique widely used in the cereals industry for proximate analysis, is a logical candidate for measuring contamination level and thus is the subject of this study. Two sets of wheat samples, harvested, prepared and scanned one year apart, were used to evaluate the NIR concept. One year consisted of nine pairs of conventional:waxy preparations, with each preparation consisting of 29 binary mixtures ranging in conventional wheat fraction (by weight) of 0-100% (261 spectral samples). The second year was prepared in the same fashion, with 12 preparations, thus producing 348 spectral samples. One year's samples were controlled for protein content and moisture level between pair components in order to avoid the basis for the conventional wheat fraction models being caused by something other than spectral differences attributed to waxy and nonwaxy endosperm. Likewise the second year was controlled by selection of conventional wheat for mixture preparation based on either protein content or cluster analysis of principal components of candidate spectra. Partial least squares regression, one and two-term linear regression, and support vector machine regression models were examined. Validation statistics arising from sets within the same year or across years were remarkably similar, as were those among the three regression types. A single wavelength on second derivative transformed spectra, namely 2290 nm, was effective at estimating the mixture level by weight, with standard errors of performance in the 6-9% range. Thus, NIR spectroscopy may be used for measuring conventional hard wheat 'contamination' in waxy wheat at mixture levels above 10% w/w.

High-Speed Optical Sorting of Soft Wheat for Reduction of Deoxynivalenol.

Fusarium head blight (FHB) is a fungal disease that affects small cereal grains, such as wheat and barley, and is becoming more prevalent throughout much of the world's temperate climates. The disease poses a health risk to humans and livestock because of the associated production of the mycotoxin deoxynivalenol (DON or vomitoxin) by the causal organism, Fusarium graminearum. A study was undertaken to examine the efficiency of high-speed, optical sorting of intact wheat (Triticum aestivum) kernels for reduction of DON concentration. Soft red winter (n = 32) and soft white (n = 3) wheat samples, known to have elevated levels of FHB, were obtained from commercial mills throughout the eastern United States. An additional seven samples of wheat from the discard piles of in-mill cleaners were also studied. Fusarium-damaged wheat, cleaned of nonkernels and foreign material ( ~4.5 kg/sample, DON range = 0.6 to 20 mg/kg), was fed into a commercial high-speed bichromatic sorter operating at a throughput of 0.33 kg/(channel-min) and a kernel rejection rate of 10%. A wavelength filter pair combination of 675 and 1,480 nm was selected for sorting, based on prior research. Visual measurements of the proportion of Fusarium-damaged kernels were collected on incoming and sorted seed (separate analyses of accepted and rejected portions), as were measurements of DON concentration. Results indicated that the fraction of DON contaminant level in the sorted wheat to that in the unsorted wheat ranged from 18 to 112%, with an average of 51%. Nine of the 35 regular samples and all seven of the discard pile samples underwent a second sort, with five from this second set undergoing a third sort. Multiple sorting was effective in producing wheat whose DON concentration was between 16 and 69% of its original, unsorted value.

Examination of spectral pretreatments for partial least-squares calibrations for chemical and physical properties of wheat.

Use of near-infrared (NIR) diffuse reflectance on ground wheat meal for prediction of protein content is a well-accepted practice. Although protein content has a strong bearing on the suitability of wheat (Triticum aestivum L.) for processed foods, wheat quality, as largely influenced by the configuration and conformation of the monomeric and polymeric endosperm storage proteins, is also of great importance to the food industry. The measurement of quality by NIR, however, has been much less successful. The present study examines the effects and trends of applying mathematical transformations (pretreatments) to NIR spectral data before partial least-squares (PLS) regression. Running mean smooths, Savitzky-Golay second derivatives, multiplicative scatter correction, and standard normal variate transformation, with and without detrending, were systematically applied to an extensive set of hard red winter wheat and hard white wheat grown over two seasons. The studied properties were protein content, sodium dodecyl sulfate (SDS) sedimentation volume, number of hours during grain fill at temperature <24 degrees C, and number of hours during grain fill at temperature >32 degrees C. The size of the convolution window used to perform a smooth or second derivative was also examined. The results indicate that for easily modeled properties such as protein content, the importance of pretreatment was lessened, whereas for the more difficult-to-model properties, such as SDS sedimentation volume, wide-window (>20 points) smooth or derivative convolutions were important in maximizing calibration performance. By averaging 30 PLS cross-validation trial statistics (standard error) for each property, we were able to ascertain the inherent modeling ability of each wheat property.

Starch waxiness in hexaploid wheat (Triticum aestivum L.) by NIR reflectance spectroscopy.

Wheat (Triticum aestivum L.) breeding programs are currently developing varieties that are free of amylose (waxy wheat), as well as genetically intermediate (partial waxy) types. Successful introduction of waxy wheat varieties into commerce is predicated on a rapid methodology at the commodity point of sale that can test for the waxy condition. Near-infrared (NIR) reflectance spectroscopy, one such technology, was applied to a diverse set of hard winter (hexaploid) wheat breeders' lines representing all eight genotypic combinations of alleles at the wx-A1, wx-B1, and wx-D1 loci. These loci encode granule-bound starch synthase, the enzyme responsible for amylose synthesis. Linear discriminant analysis of principal components scores 1-4 was successful in identifying the fully waxy samples at typically greater than 90% accuracy; however, accuracy was reduced for partial and wild-type genotypes. It is suggested that the spectral sensitivity to waxiness is due to (1) the lipid-amylose complex which diminishes with waxiness, (2) physical differences in endosperm that affect light scatter, or (3) changes in starch crystallinity.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

Hydrolysis of wheat starch and its effect on the falling number procedure: mathematical model.

A population balance model was developed for wheat starch hydrolysis to simulate the performance parameters of a viscosity-based device, known as the Falling Number instrument. The instrument is widely used as an indirect means to gauge the level of preharvest sprout activity in cereal grains such as wheat and barley. The model consists of three competing kinetics: starch gelatinization, enzymatic hydrolysis, and enzyme thermal deactivation. Using established principles of starch rheology and fluid mechanics, the model simulates the velocity profiles of the falling stirrer, starch gel viscosity, and the Falling Number readings at various levels of alpha-amylase. Model predictions for the velocity of the stirrer at any time during the downward fall, as well as the prediction of the total time needed for the fall, defined as the Falling Number, were in fair agreement with experimental measurements. There was better agreement between the modeled viscosity and the final viscosity of the starch gel as measured by a precision rheometer than there was with the measured Falling Number.