Fourier-transform infrared (FTIR) imaging has emerged as a powerful analytical tool, combining the chemical specificity of infrared spectroscopy with spatial resolution for detailed material characterization. A critical advancement in this field is the integration of focal plane array (FPA) detectors, which enable rapid, high-resolution imaging across wide sample areas. Unlike traditional single-point FTIR, which sequentially analyzes small regions, FPA-based systems capture thousands of spectra simultaneously, significantly improving throughput and enabling large-area mapping without compromising data quality.

FPA detectors consist of an array of infrared-sensitive pixels, typically made from mercury cadmium telluride (MCT) or indium antimonide (InSb), each capable of independently recording spectral data. Common configurations include 64x64, 128x128, or 256x256 pixel arrays, with higher pixel densities providing finer spatial resolution. The detector operates in a step-scan or rapid-scan mode, synchronized with an interferometer to collect interferograms that are later Fourier-transformed into spectra. This parallel acquisition drastically reduces measurement times—large samples that would require hours with single-point mapping can be imaged in minutes.

Hyperspectral data analysis plays a pivotal role in extracting meaningful information from FTIR imaging datasets. Each pixel in an FPA image contains a full infrared spectrum, typically spanning 4000 to 600 cm⁻¹, resulting in millions of data points for a single scan. Multivariate analysis techniques, such as principal component analysis (PCA) and partial least squares regression (PLS), are employed to identify spectral variations correlated with chemical composition, crystallinity, or molecular orientation. Cluster analysis methods, like k-means or hierarchical clustering, segment the image into chemically distinct regions without prior knowledge of sample composition. These approaches are particularly valuable for complex heterogeneous samples where manual inspection is impractical.

In pharmaceutical quality control, FTIR imaging with FPA detectors is widely adopted for verifying drug formulation homogeneity and detecting counterfeit products. Tablets and powders can be analyzed non-destructively to assess active pharmaceutical ingredient (API) distribution, excipient blending uniformity, and polymorphic forms. For example, variations in API concentration as low as 5% w/w can be resolved spatially, ensuring compliance with regulatory standards. Coating thickness and integrity in controlled-release formulations are also monitored, as uneven coatings may lead to inconsistent drug delivery. The technique’s sensitivity to moisture ingress further aids in stability testing, identifying deliquescence or hydrate formation in solid dosages.

Biomedical tissue imaging represents another major application, leveraging FTIR’s ability to probe biochemical changes at a molecular level. Fixed or frozen tissue sections are commonly analyzed to map distributions of proteins, lipids, nucleic acids, and carbohydrates without staining. The amide I and II bands (1700–1500 cm⁻¹) provide insights into protein secondary structure, while lipid ester carbonyl stretches (1740 cm⁻¹) highlight adipose regions. Hyperspectral data can distinguish diseased from healthy tissues based on spectral markers—for instance, altered glycogen levels in liver disease or collagen cross-linking in fibrosis. Researchers have demonstrated the utility of FTIR imaging in cancer diagnostics, where spectral differences between tumor and normal tissues enable automated classification algorithms with high accuracy.

Spatial resolution in FTIR imaging is diffraction-limited, typically ranging from 1 to 20 µm depending on wavelength and optical setup. At longer infrared wavelengths, resolution decreases, but synchrotron-based sources or optical photothermal infrared (O-PTIR) techniques can overcome this limitation for nanoscale imaging. However, FPA systems remain the preferred choice for mesoscale analysis (10 µm to several mm), balancing resolution and field of view.

Data preprocessing is essential to mitigate artifacts such as scattering effects or atmospheric absorption. Common steps include baseline correction, normalization, and atmospheric compensation (e.g., removing CO₂ and H₂O vapor bands). Advanced algorithms like extended multiplicative signal correction (EMSC) further enhance spectral comparability by accounting for physical light interactions with the sample.

Emerging trends include integrating machine learning for real-time spectral classification and combining FTIR imaging with other modalities like atomic force microscopy (AFM) or mass spectrometry for correlative analysis. These multimodal approaches provide complementary structural and chemical insights, particularly in complex biological systems.

Despite its advantages, FTIR imaging faces challenges, including large data storage requirements and computational demands for processing hyperspectral datasets. Additionally, samples must be thin (typically <20 µm for transmission mode) to avoid saturation effects, though attenuated total reflection (ATR) imaging mitigates this for thicker specimens.

In summary, FTIR imaging with FPA detectors and hyperspectral analysis has revolutionized spatially resolved chemical characterization. Its non-destructive nature, coupled with high throughput and molecular specificity, makes it indispensable in pharmaceuticals and biomedicine. Continued advancements in detector technology and data analytics promise further expansion into real-time monitoring and clinical diagnostics.