Tag Archives: screening

Brian Sharp, SafetyChain Software
FST Soapbox

How Are Companies Impacted by Labor Shortages?

By Brian Sharp
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Brian Sharp, SafetyChain Software

Food and beverage manufacturers are seeing the effects of the coronavirus when it spreads through their workforce. Recently, there have been multiple closures of facilities operated by meat processors, including Smithfield Foods and Tyson Foods as COVID-19 has infected hundreds of workers.

The backdrop of stressful operations and work: Employees now face increased questions before entering plants and feelings of isolation as lunches and breaks are now solo activities due to social distancing. All of these stressors are compounded when you think about what we’re asking them to do: Go into work and keep food on the grocery store shelves. This is a completely new way to operate, and it has a very real emotional effect on our workers.

We’ve received reports from customers where management is getting out of the back office and putting on hairnets to work the production line. The shortage of workers is a very real problem, and our customers are rising to the challenge. Plus, managing this overall labor shortage while doing more safety and sanitation checks than ever before to make sure transmission risks are eliminated is putting stress on everyone working in plants. It’s never been harder to work in the food industry.

In response to California Governor Gavin Newsom’s actions related to the pandemic, we stand behind any effort that is taken to accommodate the needs of these vital, valuable workers, including the executive order to provide supplemental paid sick leave. Such actions, both locally here in California and at the federal level, are critical to elevating the safety of our food manufacturing and distribution workers. Some heroes wear hairnets.

Temp Workers and Lack of Training Protocols

COVID-19 has had a significant impact on the availability of skilled workers in food facilities. Through all the layoffs stemming from the economic standstill, food manufacturers and grocery workers are reporting increases in hiring to help keep up with demand—and to mitigate the effects of sick employees going on quarantine for two weeks. For instance, Albertson’s, a large food grocery chain store, reported that it was hiring for 2,000 positions.

But hiring temporary workers is only half the battle. The task of training people who may have never worked in grocery or food manufacturing has become more critical in the face of new demands on sanitation and social distancing. With these measures in place, it’s no longer a case of a new employee showing up for work and shadowing another employee or supervisor. Technology can close the gap, especially in food production where the regulations and safety standards require strict adherence to processes. For example, software can facilitate shorter employee training in the areas of quality policies and good documentation practices.

Same Volume with Fewer Workers

We are working closely with customers and partners to cope with new guidelines for social distancing inside food facilities, providing the capability to do remote audits as visitor restrictions have increased. Our software is also being used to screen food manufacturing workers for symptoms of COVID-19 before shift work starts to help prevent the spread of the coronavirus to other essential workers.

In response to increased needs from customers, we have developed three solutions to address the impact of COVID-19. These solutions, which include a personnel screener, changeover manager and remote supplier auditor, can help food and beverage manufacturers efficiently manage physical distancing measures, symptom screening, and travel restrictions.

It can’t be stressed enough: The people who carry out food safety protocols are doing more checks and using more labor time to conform to regulations and guidelines for COVID-19. And, adhering to the systems, regulations and processes used to promote safe, high-quality products (in the same or even higher volumes) remains as crucial as ever. Simplifying these processes by leveraging software has been shown to cut 8 to12 hours of labor per day for a single facility. This is critical at a time when even one person being sick can cause lower throughput.

Plus, this isn’t like manufacturing a car where a line will be built to produce hundreds of thousands of cars over a two- to three-year period. Food manufacturers must often change a line over to produce a different flavor, package type or food type altogether, in as little time as possible to keep production going. Robots and automation can help, but in a crisis like this where immediate productivity gains are needed, software can make the much-needed difference.

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Food Fraud and Adulteration Detection Using FTIR Spectroscopy

By Ryan Smith, Ph.D.
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Producers of food-based products are faced with challenges of maintaining the safety and quality of their products, while also managing rapid screening of raw materials and ingredients. Failure to adequately address both challenges can be costly, with estimated recall costs alone starting around $10 million, in addition to any litigation costs.1 Long-term costs can accumulate further as a result of damage to brand reputation. A vast array of methods has been employed to meet these challenges, and adoption continues to increase as technology becomes smaller, cheaper and more user friendly. One such technique is Fourier transform infrared (FTIR) spectroscopy, an analytical technique that is widely used for quick (typically 20–60 seconds per measurement) and non-destructive testing of both man-made and natural materials in food products. The uniformity and physical state of the sample (solid vs. liquid) will dictate the specifics of the hardware used to perform such analyses, and the algorithm applied to the identification task will depend, in part, on the expected variability of the ingredient.

Infrared spectral measurements provide a “compositional snapshot”— capturing information related to the chemical bonds present in the material. Figure 1 shows an example of a mid-infrared spectrum of peppermint oil. Typically, the position of a peak along the x-axis (wavenumber) is indicative of the type of chemical bond, while the peak height is related either to the identity of the material, or to the concentration of the material in a mixture. In the case of peppermint oil, a complex set of spectral peaks is observed due to multiple individual naturally occurring molecular species in the oil.

Mid-infrared spectrum, peppermint oil
Figure 1. Mid-infrared spectrum of peppermint oil. The spectrum represents a “chemical snapshot” of the oil, as different peaks are produced as a result of different chemical bonds in the oil.

Once the infrared spectrum of an ingredient is measured, it is then compared to a reference set of known good ingredients. It is important that the reference spectrum or spectra are measured with ingredients or materials that are known to be good (or pure)—otherwise the measurements will only represent lot-to-lot variation. The comparative analysis can assist lab personnel in gaining valuable information—such as whether the correct ingredient was received, whether the ingredient was adulterated or replaced for dishonest gain, or whether the product is of acceptable quality for use. The use of comparative algorithms for ingredient identification also decreases subjectivity by reducing the need for visual inspection and interpretation of the measured spectrum.

Correlation is perhaps the most widely used algorithm for material identification with infrared spectroscopy and has been utilized with infrared spectra for identification purposes at least as early as the 1970s.2 When using this approach, the correlation coefficient is calculated between the spectrum of the test sample and each spectrum of the known good set. Calculated values will range from 0, which represents absolutely no match (wrong or unexpected material), to 1, representing a perfect match. These values are typically sorted from highest to lowest, and the material is accepted or rejected based on whether the calculated correlation lies above or below an identified threshold. Due to the one-to-one nature of this comparison, it is best suited to identification of materials that have little or no expected variability. For example, Figure 2 shows an overlay of a mid-infrared spectrum of an ingredient compared to a spectrum of sucrose. The correlation calculated between the two spectra is 0.998, so the incoming ingredient is determined to be sucrose. Figure 3 shows an overlay of the same mid-infrared spectrum of sucrose with a spectrum of citric acid. Notable differences are observed between the two spectra, and a significant change in the correlation is observed, with a coefficient of 0.040 calculated between the two spectra. The citric acid sample would not pass as sucrose with the measurement and algorithm settings used in this example.

Mid-infrared spectrum, sucrose
Figure 2. An overlay of the mid-infrared spectrum of sucrose and a spectrum of a different sample of sucrose.
Mid-infrared spectrium, sucrose, citric acid
Figure 3: An overlay of the mid-infrared spectrum of sucrose and a spectrum of citric acid.

When testing samples with modest or high natural variability, acceptable materials can produce a wider range of infrared spectral features, which result in a correspondingly broad range of calculated correlation values. The spread in correlation values could be of concern as it may lead to modification of algorithm parameters or procedures to “work around” this variation. Resulting compromises can increase the potential for false positives, meaning the incorrect ingredient or adulterated material might be judged as passing. Multivariate algorithms provide a robust means for evaluating ingredient identity for samples with high natural variability.

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