## 1 Introduction  
_Salmonella_ is a leading cause of gastroenteritis, with severe cases occasionally resulting in death. The World Health Organization estimates that 550 million people fall ill to foodborne diseases annually, with 33 million healthy life years calculated as lost. Non-tynboidal _Salmonella_ represents one of the four primary pathogenic bacteria responsible [1]. Traditional detection methods such as the use of a nutrient enriched growth medium or polymerase chain reaction (PCR) have been used as the standard for the detection of _Salmonella_ for years. While these methods are effective, the incubation time required for nutrient enriched growth media, or the reoccurring costs along with the advanced training requirement of PCR are disadvantages that influence the time required to correctly identify the causative agent and source of a foodborne disease outbreak.  
In recent years, hyperspectral imaging (HSI) and hyperspectral microscope images (HMI) have been approached for food safety and quality assessment. HSI methods have been applied for the estimation of bacterial total viable counts (TVC) on the surface of salmon, pork, and chicken cuts [2; 3; 4]. HSI has seen application for the determination of the _Campylobacter_ species or Shiga toxin-producing _E. coli_. (STEC) serogroup of bacterial colonies grown on their respective selective nutrient enriched agar plates [5; 6; 7]. [PERSON] et al. [8] discovered that an HMI system could differentiate between spectral patterns of viable and non-viable _Bacillus anthrac_ spores damaged from contact with hydrogen peroxide. Previously, our laboratory's research has shown that bacterial species can be differentiatedthrough HMI, as well as serotypes of the same species, by using a single cell-based mean pixel intensity pattern, and early detection was possible in times of 8 h or less [9].  
The previous project objectives involved the use of discriminant analyses (DA) or other multivariate approaches to determine if the differences in a specific experimental treatment existed. In order to advance this technology forward, an unsupervised classification approach for HMI data is necessary to determine a taxonomical identification. Presumptive pathogen screening of a food sample would require HMI to produce a yes/no answer, similar to qualitative PCR. In order to construct an unsupervised prediction model that results in a bacterial HMI slide testing positive or negative for the presence of _Salmonella_, a soft independent modelling of class analogy (SIMCA) approach was chosen. In this application, SIMCA was preferable to a DA with hard decision boundaries, because DA will force a sample into a positive or negative category, whereas the soft boundaries of SIMCA can reject a sample outside of the calibration model's boundaries [10]. This is preferable for a qualitative food safety approach that gives a binary yes or no determination for a bacteria's presence in a food product. If a DA forces a sample into a false-negative or type II error, a potentially contaminated product can be overlooked and erroneously regarded as safe, jeopardizing public health. Previously, food authentication research has addressed the issue of a food product's quality by using spectroscopy methods paired with SIMCA to determine if product adulterations have been made for economic benefit [11].  
HMI research has shown potential for application in early and rapid food safety methodologies, but the validation of a comprehensive and robust modeling approach is necessary in moving the unsupervised classification technology forward. Data were collected over the span of five years, between May 2012 and May 2017. The aim of this study was that a robust one-class SIMCA calibration model for rapid _Salmonella_ prediction at a cellular level was constructed to determine if validation from a multi-year study could accurately predict _Salmonella_ presence at a comparable performance to traditional detection methods such as PCR and nutrient enriched plating, with approximately 95% accuracy.