Agriculture – Assessing the Quality of Corn Silage Using an Electronic Nose

Traditional electronic noses, also known as eNoses, create a familiar response pattern using a range of dissimilar but not specific chemical sensors. Developers of artificial intelligence algorithms and neural networks were interested in eNoses for some time, but physical instability and overlapping responses of physical sensors have considerably limited their performance. In addition, eNoses cannot quantify or separate the chemistry of aromas.

zNose® is a new type of electronic nose based on ultra-fast gas chromatography. It simulates an almost infinite number of specific virtual chemical sensors, and creates olfactory images based upon the chemistry of aromas.

The zNose® can carry out analytical measurements of odors and volatile organic vapors in near real time with part per trillion sensitivity. Both separation and quantification of the individual chemicals within an odor is carried out in a fraction of seconds. Picogram sensitivity, electronically variable sensitivity and universal non-polar selectivity are obtained using a patented solid-state mass-sensitive detector.

The instrument, featuring an integrated vapor preconcentrator and the electronically variable detector, measures vapor concentrations spanning 6+ orders of magnitude. Figure 1 shows zNose®, a portable and useful tool used for assessing the quality of aromatic food products such as wheat silage and corn.

Portable zNose® technology incorporated into a handheld instrument

Figure 1. Portable zNose® technology incorporated into a handheld instrument

How the zNose™ Quantifies the Chemistry of Aromas

Figure 2 shows a simplified diagram of the zNose® system that consists of two sections. One section uses a capillary tube (GC column), helium gas and a solid state detector, while the other section contains a heated inlet and pump which samples ambient air.

The two sections is linked by a “loop” trap which serves as an injector when placed in the helium section (inject position) and as a preconcentrator when placed in the air section (sample position). Operation involves a two step process. First, aroma or ambient air is sampled and organic vapors are collected (preconcentrated) on the trap.

Once sampling is over, the trap is transferred into the helium section where the organic compounds that were collected are injected into the helium gas. These organic compounds then pass via a capillary column with varying velocities and therefore separate chemicals exit the column at characteristic times. As the chemicals exit the column, they are detected and measured by a solid state detector.

The collection of sensor data is controlled by an internal high speed gate array microprocessor and this data is transferred to a computer or user interface using a USB or RS-232 connection.

Simplified diagram of the zNose® showing an air section on the right and a helium section on the left. A loop trap preconcentrates organics from ambient air in the sample position and injects them into the helium section when in the inject position.

Figure 2. Simplified diagram of the zNose® showing an air section on the right and a helium section on the left. A loop trap preconcentrates organics from ambient air in the sample position and injects them into the helium section when in the inject position.

Figure 3 shows aroma chemistry, which can be shown as a polar olfactory image or a sensor spectrum of odor intensity versus retention time. A a single n-alkane vapor standard is used to achieve calibration. A library of retention times of known chemicals indexed to the n-alkane response (Kovats indices) enables compound identification and machine independent measurement.

Sensor response to n-alkane vapor standard, here C6-C14, can be displayed as sensor output vs time or its polar equivalent olfactory image

Figure 3. Sensor response to n-alkane vapor standard, here C6-C14, can be displayed as sensor output vs time or its polar equivalent olfactory image.

Chemical Analysis (Chromatography)

Figure 3 shows the time derivative of the sensor spectrum that yields the spectrum of column flux, also known as a chromatogram. Shown in Figure 4 is the chromatogram response of n-alkane vapors (C6 to C14) that gives an accurate measure of retention times.

Graphically defined regions indicated as red bands in Figure 4 calibrate the system and gives a reference time base against which subsequent chemical responses are indexed or compared. For instance, a response midway between C10 and C11 would possess a retention time index of 1050.

Chromatogram of n-alkane vapors (C6 to C14)

Figure 4. Chromatogram of n-alkane vapors (C6 to C14)

Corn Silage Quality

Silage refers to storage and partial fermentation of forage plants such as corn or wheat in a silo. For Instance, fodder (livestock feed) is prepared and stored in an airtight structure that presses the crops.

Fermented crops with nutritional value and increased palatability for animals can be stored for a long period of time. Corn fermentation mainly produces lactic acid, which gives a sour note to the fermented corn.

Silage is produced in Silos

Figure 5. Silage is produced in Silos

Corn kernels that contain any amount of mold (Figure 6) are believed to be damaged. Moisture can promote the growth of molds and toxic substances that are released by fungi and mold, such as fusarium moniliform (moldy corn disease) and aflatoxins (aflatoxicosis); These can be potentially lethal if ingested. Fungus and molds release odors, which contain microbial volatile organic compounds (MVOC). These compounds are perceived by humans as musty smells.

Moldy corn kernels

Figure 6. Moldy corn kernels

Testing Sour Corn Samples

Two different types of samples (No. 170 and No. 101) of corn kernels considered to be sour were investigated. The test protocol involved placing about 5 g of corn into a 40 mL vial sealed with a septa lid in order to contain headspace vapors.

The vials were thermostated 5 minutes at a temperature of 40 oC before measuring the chemicals within the headspace with a zNose®. A side-ported sampling needle was used to pierce the septa of each vial and vapors were sampled.

Chromatogram from sour corn sample 170

Figure 7. Chromatogram from sour corn sample 170

Figures 7 and 8 show the chromatogram results for each of two corn samples, along with a tabulation of detected compound indices and concentrations (peak area in counts). The vertical scale of Figure 8 and Figure 9 is 50,000 counts/second and 20,000 counts/second, respectively.

The sour odor produced by these samples is because of organic acids such as lactic acid depicted as peak G and peak D in Figures 7 and 8, respectively. Sample 101 had a less lactic acid concentration at 2784 counts, while sample 170 was considerably more at 5587 counts. Also, sample 170 contained two other high concentration compounds at more than 5000 counts at indices of 1068 and 1178. The latter is considered to be isoborneol, which has a musty odor.

Chromatogram from sour corn sample 101

Figure 8. Chromatogram from sour corn sample 101.

Testing Moldy Corn Samples

To test moldy corn samples, the headspace vapors from a sample considered to be moldy (no. 150) was first tested and found to release several compounds that do not exist in other corn samples; the chromatogram is depicted in Figure 9.

With an index of 1135, compound G is considered to be 3-octanol and exhibited a concentration count of 2992. Compound J (index=1310) exhibited a concentration count of 5482 and is considered to be either undecanal or indole which have pungent odors. Isoborneol has a musty odor, and this compound was also present (peak H) but at a relatively lower concentration count of 294.

Chromatogram of moldy corn sample No. 150

Figure 9. Chromatogram of moldy corn sample No. 150.

Testing Good Corn

For this test, headspace chemistry from two different batches of good corn were assessed and found to have extremely low concentrations or odor (Figures 10 and 11). Only trace amounts of lactic acid (468, 205 counts) were identified. While trace amounts of compounds indicative of mold were detected, both samples had concentration counts below 1000.

Chromatogram of GOOD corn sample No. 1

Figure 10. Chromatogram of GOOD corn sample No. 1.

Chromatogram of GOOD corn sample No.

Figure 11. Chromatogram of GOOD corn sample No. 2.

Comparing Corn Odor Chromatograms

For comparison purposes, vertically offset chromatograms from all five corn samples tested are illustrated in Figure 12. While the lactic acid peak is easily observed in the SOUR samples, the distinctive compound peaks of MUSTY corn are not fully seen in other samples. In addition, the relatively odor free chromatograms of both GOOD corn samples are also in stark contrast.

Vertically offset chromatograms from corn samples

Figure 12. Vertically offset chromatograms from corn samples.

Comparing Corn Odor Olfactory Images

For comparison purposes, olfactory images (Vaporprints®) based on the derivative chromatograms from all five corn samples tested are illustrated in Figure 13. Olfactory images provide an easier means of comparing odors.

The lactic acid peak is clearly observed in the SOUR samples and so are the distinctive compound peaks of MOLDY corn. Since GOOD corn samples have low odor concentration, their relatively odor-free chromatograms provide only small images.

Vaporprint® images based upon corn chromatograms

Figure 13. Vaporprint® images based upon corn chromatograms.

Conclusion

Chemical profiling corn samples considered to be GOOD, SOUR, and MUSTY have shown that quality can be quantitively judged in a fast and efficient way depending on the unique chemistry of each odor. It was shown that good corn produces very little odor and as a result low chemical concentrations of target analytes, but poor quality corn which is either moldy or partially fermented generates odors with relatively high concentrations of target compounds.

In addition, indexing of retention times for target compounds using an n-alkane odor standard provides an easy way to detect and prevent the complications of using multiple standards in the field.

Dynamic headspace analysis employing ultra-high speed gas chromatography can be combined with sensory data to achieve an objective way of classifying silage such as wheat, corn, soybeans and sorghum. The sensory and chemical data can be subjected to multivariate analysis such as partial least squares (PLS) and principal component analysis (PCA) methods to establish which volatiles are best used to classify quality.

Use of optimized variables (compounds indicating off-odors) and appropriate choice of samples as well as preprocessing of chemical data such as scaling, transformation and normalization can be employed for quality assessment. Samples with distinct mixed odors, that is, having smoky, sour, insect or musty odors can be used along with quantitative chemical analyses.

The zNose® has the portability, accuracy, precision and speed required for cost-effective field measurements. Since such measurements are based on established chromatographic methods, they can be easily confirmed through independent laboratory testing.

This information has been sourced, reviewed and adapted from materials provided by Electronic Sensor Technology.

For more information on this source, please visit Electronic Sensor Technology.

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