# Refining near-infrared spectroscopy for collagen quantification: A new predictive model for archaeological bone

Christina Ryder, Gerardo Celis, Thibaut Devièse, Sahra Talamo, Katerina Douka, Matthew Collins, Angela Perri, Heather Thakar, William Pestle, Matt Sponheimer

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## To cite this version:

Christina Ryder, Gerardo Celis, Thibaut Devièse, Sahra Talamo, Katerina Douka, et al. Refining near-infrared spectroscopy for collagen quantification: A new predictive model for archaeological bone. Journal of Archaeological Science, 2026, 185, pp.106448. 10.1016/j.jas.2025.106448. hal-05438130

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# Refining near-infrared spectroscopy for collagen quantification: A new predictive model for archaeological bone

Christina Ryder<sup>a,b,*</sup>, Gerardo Celis<sup>c</sup>, Thibaut Devièse<sup>d</sup>, Sahra Talamo<sup>e</sup>, Katerina Douka<sup>f</sup>, Matthew Collins<sup>g,h</sup>, Angela Perri<sup>i</sup>, Heather Thakar<sup>j</sup>, William Pestle<sup>k</sup>, Matt Sponheimer<sup>a</sup>

## ABSTRACT

Collagen is a vital archaeological material, preserving biochemical signatures that provide insights into past environments, diets, and human-animal interactions. However, diagenesis can lead to rapid and inconspicuous collagen degradation. Given the variability in collagen preservation and its significance for analyses such as radiocarbon dating, stable isotope analysis, and ZooMS, researchers have developed prescreening techniques to assess collagen preservation before destructive sampling. Current prescreening approaches, including %N and C:N ratios, typically require sample destruction and access to equipped laboratories. Spectroscopic techniques such as Raman spectroscopy and Fourier Transform Infrared spectroscopy have been explored as alternatives, but they are limited in penetration depth, generalizability (at present at least), and are often still destructive, if minimally.

Here, we further develop single-point near-infrared (NIR) spectroscopy as a fully non-destructive, rapid, and field-portable method for prescreening bone for collagen preservation. Unlike FTIR and Raman spectroscopic techniques, NIR light penetrates below the surface of bone, enabling assessment of internal collagen preservation without destructive sample preparation. Using Partial Least Squares Regression (PLSR) and Random Forest (RF) modeling, we trained predictive models on whole bones with known collagen yields and validated the models on an independent archaeological collection. Both PLSR and RF models, when restricted to the 2030–2060 nm range, demonstrate strong and comparable performance while avoiding wavelengths associated with consolidants in our reference library. The models outperform traditional % N-based methods in identifying suitable samples for radiocarbon dating. These models enable the high-throughput screening of large collections of bone, improving sample selection and minimizing unnecessary destructive analysis.

## 1. Introduction

Collagen is the most frequently analyzed protein in archaeological science, with applications spanning radiocarbon dating, stable isotope analysis, and proteomics. It is the dominant organic component of bone, comprising approximately 85–90 % of the total organic matrix, and is essential for the physical integrity of skeletal remains. In living bone, this collagen is embedded in a composite matrix of mineral, water, and other proteins, with hydroxyapatite (a calcium phosphate mineral)

forming the primary inorganic fraction (Collins et al., 2000; Currey, 2002; Leaver, 1979; Lian et al., 1978; Price et al., 1976; Reiche et al., 2002; Smith et al., 2005; Zioupos et al., 1999). Upon burial, collagen's persistence in the archaeological record is highly variable, but its survival underpins a range of critical analyses in human evolutionary and archaeological research.

Radiocarbon dating of collagen has made it possible to chronologically anchor some of the most significant events in human prehistory, including the earliest appearances of *Homo sapiens* in Europe and Siberia

<footnote><sup>a</sup> Department of Anthropology, University of Colorado Boulder, 1350 Pleasant St., Boulder, CO, USA</footnote>

<footnote><sup>b</sup> Center for the Study of the First Americans, Department of Anthropology, Texas A&M University, College Station, TX, 77843-4352, USA</footnote>

<footnote><sup>c</sup> Department of Anthropology, University of Arkansas, Main 109A, Fayetteville, AR, USA</footnote>

<footnote><sup>d</sup> CEREGE, Aix-Marseille Univ, CNRS, IRD, INRAE, College de France, Aix-en-Provence, France</footnote>

<footnote><sup>e</sup> Department of Chemistry, University of Bologna, Via Francesco Selmi, 2, 40126, Bologna, BO, Italy</footnote>

<footnote><sup>f</sup> Department of Evolutionary Anthropology, University of Vienna, Djerassipl. 1, 1030, Wien, Austria</footnote>

<footnote><sup>g</sup> Department of Archaeology, University of Cambridge, Downing Street, Cambridge, CB2 3DZ, UK</footnote>

<footnote><sup>h</sup> Globe Institute, Section for Geobiology, University of Copenhagen, Øster Farimagsgade 5, Bygning 7, 1353, København K, Denmark</footnote>

<footnote><sup>i</sup> Chronicle Heritage Arabia, 2025 North 3rd Street, Suite 157, Phoenix, AZ, 85004, USA</footnote>

<footnote><sup>j</sup> Department of Anthropology, Texas A&M University, College Station, TX, 77843-4352, USA</footnote>

<footnote><sup>k</sup> Kelsey Museum of Archaeology, University of Michigan, 434 South State Street, Ann Arbor, MI, 48109, USA</footnote>

<footnote>* Corresponding author. Department of Anthropology, University of Colorado Boulder, 1350 Pleasant St., Boulder, CO, USA.</footnote>

<footnote>E-mail addresses: christina.ryder@colorado.edu (C. Ryder), gerardoc@uark.edu (G. Celis), deviese@cerege.fr (T. Devièse), sahra.talamo@unibo.it (S. Talamo), katerina.douka@univie.ac.at (K. Douka), matthew@palaeome.org (M. Collins), aperri@chronicleheritage.com (A. Perri), thakar@tamu.edu (H. Thakar), wjpestle@umich.edu (W. Pestle), matt.sponheimer@colorado.edu (M. Sponheimer).</footnote>
(Benazzi et al., 2011; Fu et al., 2014; Higham et al., 2009, 2011; Hublin et al., 2020; Mylopotamitaki et al., 2024; Prüfer et al., 2021), admixture with Neanderthals (Fu et al., 2014, 2015; Trinkaus et al., 2003), and the eventual disappearance of *H. neanderthalensis* (Abrams et al., 2024; Devièse et al., 2017, 2021; Higham et al., 2014; Zilhão et al., 2017). In parallel, stable isotope analysis of bone collagen has illuminated paleodietary patterns across time and space—ranging from the adoption of maize in the Americas to marine resource use (Bender et al., 1981; Richards and Hedges, 1999; Vogel and Van Der Merwe, 1977). More recently, collagen-based proteomics—such as Zooarchaeology by Mass Spectrometry (ZooMS)—has enabled the identification of otherwise indistinguishable bone fragments, even revealing hominin remains from fragmentary assemblages (Brown et al., 2016; Devièse et al., 2017; Welker et al., 2016).

Despite its value, collagen preservation is often compromised by diagenetic processes that begin shortly after death. These processes can vary dramatically across—and even within—archaeological sites, leading to inconsistent preservation (Collins et al., 3/1995; Jans et al., 2002). As a result, identifying bones with sufficient collagen for analysis frequently requires the destructive sampling of many specimens, some of which may be of cultural or scientific significance. This has raised ethical and practical concerns, particularly among Indigenous communities and museum curators, as faunal collections represent finite and often irreplaceable resources (Matisoo-Smith, 2019; Pálsdóttir et al., 2019; Reitz, 2009).

To address these challenges, researchers have developed and refined methods to prescreen bone for collagen preservation before full analytical processing. Elemental analysis, particularly nitrogen content (%N) and the carbon-to-nitrogen (C:N) atomic ratio, are among the most reliable indicators, with bones containing less than ~0.76 % N or falling outside the accepted C:N range (~2.9–3.6) typically unsuitable for collagen-based analysis (Ambrose, 2005, 1993, 1990; Brock et al., 2010a,b; DeNiro, 1985; Van Klinken, 1999). However, even this relatively efficient method still requires destructive sampling. Spectroscopic techniques such as Fourier-Transform Infrared (FTIR), Raman, and near-infrared (NIR) spectroscopy have thus gained attention as minimally invasive or non-destructive alternatives; however, FTIR and Raman spectroscopy are limited to surface-level analysis of only a few microns and may still require destructive sampling. Each of these methods measures specific molecular vibrations or absorbances linked to collagen or bone mineral structure, and several have shown promising correlations with collagen yield (France et al., 2014; Madden et al., 2018; Pestle et al., 2014a, 2015; Pothier Bouchard et al., 2019; Quiles et al., 2022; Sponheimer et al., 2019).

NIR spectroscopy offers significant advantages. It is entirely non-destructive, requires no sample preparation, and can be conducted rapidly with portable equipment. Notably, NIR light penetrates bone on the order of millimeters into bone, and up to approximately 8.5 mm in biological tissues (Afara et al., 2018; Faris et al., 1991), allowing assessment of internal composition without invasive sampling. Building on early studies that demonstrated its feasibility (Vincke et al., 2014), recent advances have shown NIR models can be used to quantify collagen yield in archaeological bone (Lugli et al., 2021; Malegori et al., 2023; Sponheimer et al., 2019). While single-point NIR analysis does not provide spatial mapping (see Lugli et al., 2021; Malegori et al., 2023), it enables rapid (5 s), high-throughput screening of individual specimens (over 500 per day), allowing multiple non-overlapping measurements per specimen and generating immediate results that support in-the-moment sample selection. Furthermore, previous studies using NIR hyperspectral imaging to characterize collagen preservation (e.g., Lugli et al., 2021; Malegori et al., 2023) were based on limited specimens (44 or fewer) and were constrained by small validation datasets (six or fewer specimens), which limits the generalizability of their models.

Here, we present an updated and refined set of NIR spectroscopy models for predicting collagen yield in archaeological bone. We assess

their predictive performance, examine their potential for field use, and explore methods for detecting and accounting for contamination. Our goal is to improve collagen screening workflows while minimizing the destructive impact on valuable faunal collections.

## 2. Materials and methods

### 2.1. Samples

NIR spectra (each an average of 50 scans) were collected from flat locations on the exterior surface (or periosteal surface) of whole bones. Over 500 mammalian bone samples (human and non-human), aged about 500 to 45,000 years ago and with known collagen yields, were initially analyzed for model development. However, after excluding samples contaminated with consolidant and those not undergoing ultrafiltration, the dataset was refined to 319 samples. Problematically, the collagen yield distribution of the 319 samples was heavily skewed toward the 0–2 % range (Fig. S1), necessitating adjustments to achieve a more representative dataset for analysis (Lindström et al., 2011; Workman, 2007). In this study, overrepresentation in the 0–2 % range was mitigated by reducing the samples within that interval using K-means clustering, which preserves spectral diversity while reducing sample redundancy (MacQueen, 1967). The final dataset comprised 140 independent bone samples, each free of consolidants and representing a single skeletal element, with one spectrum collector per sample (Fig. S2). Table 1 summarizes the descriptive statistics for the collagen yield of the samples in the final dataset.

### 2.2. Collagen extraction techniques

All bone samples were processed using an acid-base-acid collagen extraction protocol followed by ultrafiltration to prepare for radio-carbon dating, with samples processed at the Oxford Radiocarbon Accelerator Unit (ORAU) or the Max Planck Institute for Evolutionary Anthropology in Leipzig (MPI). Collagen extraction at each laboratory was performed using standardized, highly precise protocols, ensuring consistent yields. Both the ORAU and the MPI routinely handle highly valuable and ancient specimens, and consistently produce reliable collagen yields and radiocarbon dates. The collagen sampling and pre-treatment protocols employed by the ORAU (Brock et al., 2010a,b) and MPI (Talamo and Richards, 2011) are summarized in Table 2, with full methodological details available in the Supplemental Materials.

### 2.3. Device

NIR spectra were collected using an ASD LabSpec 4 Standard Resolution spectrometer (Malvern Panalytical), a field-ready device weighing 5.44 kg (12 lbs) and fitted with a fiber-optic probe (spot size 2 mm; see Fig. S3). The bifurcated fiber-optic probe allows simultaneous illumination and collection; one branch transmits light from an integrated broad-spectrum halogen bulb, while the other collects the reflected signal. The reflected light is transmitted back to the LabSpec 4, which covers a spectral range of 350–2500 nm, yielding 2151 variables after interpolation (refer to Table 3 for detailed specifications). The spectra were collected in reflection mode with the fiber-optic probe held perpendicular to and flush against a flat surface on the bone to maximize the reflective signal and minimize scattering variability.

<table><caption>**Table 1**

Descriptive statistics of collagen yield in the reference collection.</caption><thead><tr><th></th><th>Total</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th><th>Skewness</th></tr></thead><tbody><tr><td>Known Collagen Samples</td><td>140.00</td><td>4.58</td><td>4.74</td><td>0.00</td><td>17.20</td><td>0.91</td></tr></tbody></table>
<table><caption>**Table 2**
Sampling and pretreatment steps for ORAU and MPI.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Sampling Method</th><th colspan="3">Decalcification</th><th colspan="3">Gelatinization</th></tr><tr><th>HCl</th><th>NaOH</th><th>HCl</th><th>HCl</th><th>Ezee filter</th><th>Ultrafiltration</th><th>Freeze-Dried</th></tr></thead><tbody><tr><td>ORAU</td><td>Crushed or coarse powder</td><td>0.5 M, RT, 18hr</td><td>0.1 M, RT, 30min</td><td>0.5 M, RT, 1hr</td><td>10-3M, 75 C, 20hr</td><td>Yes; Precleaned</td><td>Yes, Precleaned Vivasin™ 15–30 kD MWCO</td><td>&gt;30 kD</td></tr><tr><td>Max Planck Leipzig</td><td>Whole bone</td><td>0.5 M, 5 °C, until complete</td><td>0.1 M, RT, 30min</td><td>0.5 M</td><td>10-3M, 75 C, 20hr</td><td>Yes; Precleaned</td><td>Yes, Precleaned Vivasin™ 15–30 kD MWCO</td><td>&gt;30 kD</td></tr></tbody></table>

<table><caption>**Table 3**
Overview of LabSpec 4 detector specifications.</caption><thead><tr><th></th><th>Detector</th><th>Spectral Range</th><th>Spectral Resolution</th><th>Sampling Interval</th></tr></thead><tbody><tr><td>VNIR</td><td>512-element silicon photo-diode array</td><td>350–1000 nm</td><td>3 nm</td><td>1.4 nm</td></tr><tr><td>SWIR 1</td><td>TE-Cooled, InGaAs photo-diode</td><td>1001–1800 nm</td><td>10 nm</td><td>2 nm</td></tr><tr><td>SWIR 2</td><td>TE-Cooled, InGaAs photo-diode</td><td>1801–2500 nm</td><td>10 nm</td><td>2 nm</td></tr></tbody></table>

## 2.4. Building the predictive models

### 2.4.1. Pretreatments

Spectra were imported into the Unscrambler X software Version 10.5.1 (AspenTech, Bedford, MA, USA) for multivariate analysis. Line plots of the untransformed spectra were generated for visual inspection to identify potential spectral outliers. Principal Component Analysis (PCA) was then conducted on the untransformed spectra across the full spectral range captured by the instrument (350 nm–2500 nm) and the NIR-specific range (780 nm–2500 nm) to detect unusual clustering patterns before pretreatment and further analysis (Mark, 2007).

To facilitate direction comparisons with existing NIR literature, the NIR spectra were transformed from reflectance (R) (Fig. 1A) to absorbance (A) (Fig. 1B), using: $A = log_{10}(\frac{1}{R})$. Because this calculation is performed on reflectance data and because bone is a highly scattering material, this transformation produces pseudo-absorbance rather than true absorbance. Nevertheless, this approach is widely used in diffuse reflectance NIR spectroscopy of scattering materials (e.g., Seidel et al., 2022; Cruz-Tirado et al., 2023).

Due to the inherent noise in the NIR spectra, primarily driven by detector limitations and light scattering within the bone matrix, pretreatment is essential before further analysis (Norris and Williams, 1984). A Savitzky-Golay smoothing and differentiation algorithm was applied to the NIR spectra using a second derivative, third-order polynomial; 31-point window (Fig. 1C). The relatively large 31-point Savitzky-Golay smoothing window was selected due to the high density of spectral variables and the inherent noise in NIR reflectance measurements of bone, aligning with similar preprocessing approaches in the literature (see Pretorius et al., 2024; Chen et al., 2011). This transformation improves spectral resolution and corrects for baseline shifts and baseline drifts, yielding well-defined downward-facing peaks that enhance spectral interpretability (Gorry, 1990; Savitzky and Golay, 1964).

### 2.4.2. Partial Least Squares Regression and wavelength selection

The wavelengths driving variation in collagen preservation include 1688 nm, 1730 nm, 2043 nm, 2175 nm, and 2275 nm. However, due to the broad and overlapping nature of NIR absorbance bands, assigning these wavelengths to individual molecular vibrations or functional groups is difficult without clear supporting evidence. Previous studies on protein and bone have identified the waveband extending from 2030 nm to 2059 nm as the second overtone of isolated C=O stretching and the

first overtone of N-H stretching (Baykal et al., 2010; Burns and Ciurczak, 2007; Miyazawa and Sonoyama, 1998; Osborne, 2000; Vincke et al., 2014; Wang et al., 1994). Additionally, the waveband extending from 2155 nm to 2195 nm is associated with combination bands of Amide I and Amide II, which arise from C=O stretching, N-H bending, and C-N stretching vibrations within proteins (Burns and Ciurczak, 2007; Osborne, 2000). Regardless of the specific features driving absorbance, both the 2043 nm and 2175 nm wavebands are characteristic of protein content, and for example, both are used to predict crude protein content in dairy products (Robert et al., 1987) see Table 4 for contributing overtone bands (Robert et al., 1987; Baykal et al., 2010; Ciurczak et al., 2021; McGoverin et al., 2016; Olumegbon et al., 2017; Palukuru et al., 2014; Workman, 2016; Workman and Weyer, 2007, 2012);).

Chemometric models were developed using Partial Least Squares Regression (PLSR) to predict collagen yield in bone, training them on the calibration dataset across various wavelength combinations. Hundreds of predictive models were developed and evaluated, incorporating various iterations based on the physical state of the bone (i.e., whole, ground, or both), as well as models that independently targeted key vibrational modes. These included models focusing on C-H stretching and combination bands (e.g., 1688 nm, 1730 nm, and 2275 nm) and models isolating N-H stretching features (e.g., 2045 nm), enabling the assessment of whether specific functional groups perform better under different preservation conditions. The PLSR models were then applied to predict collagen content of independent validation sets, including an archaeological collection with known collagen yield. Guided by the NIR principal component loadings plot, previous findings, and collagen's structure and composition (Sponheimer et al., 2019), we present PLSR using various wavelength ranges (see Table 5) to identify models that are broadly applicable across diverse archaeological sites.

### 2.4.3. Random Forest modeling

To explore alternative modeling approaches, we developed Random Forest models trained on the transformed spectral data as an independent method for predicting collagen yield. Random Forest functions by assembling an ensemble of independent decision trees, each trained on bootstrapped subsets of the data (Breiman, 2001). The algorithm evaluates each variable and determines its significance in accurately predicting the results of the Random Forest model. To evaluate the Random Forest algorithms and the influence of spectral range on model performance, two Random Forest models were developed: one using the full LabSpec4 NIR range (780–2500 nm), referred to as the 780–2500 nm Random Forest model, and another using a restricted range (2030–2060 nm), referred to as the 2045 nm Random Forest model. The restricted region was selected based on its alignment with known protein-related absorbance features and its inclusion of the strongest collagen-associated NIR absorbance features, aligning with the spectral range utilized in our preferred PLSR model (Baykal et al., 2010; Burns and Ciurczak, 2007; Miyazawa and Sonoyama, 1998; Osborne, 2000; Vincke et al., 2014; Wang et al., 1994). Each Random Forest model consists of an ensemble of 500 decision trees, with the final prediction determined by the mean output across all trees. Additional hyperparameters include a tuning length of 23 with repeated five-fold cross-validation repeated three times to assess the models. The trained Random Forest models were also applied to predict collagen yield for the
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** NIR spectra (350–2500 nm) of archaeological bone. The absorbance spectra of five archaeological bone samples, each with varying collagen content: 0.01 % (magenta), 3.22 % (light blue), 6.00 % (dark blue), 9.26 % (red), and 17.10 % (maroon). (A) Untransformed reflectance spectra. (B) NIR spectra after undergoing a simple transformation from reflectance to absorbance. (C) NIR absorbance spectra after Savitzky-Golay transformation. The Savitzky-Golay transformation (second derivative, third-order polynomial; 31 points smoothing) applies a localized least-squares fit to estimate the second derivative at each point, effectively baselining the spectra while reducing noise and preserving spectral features. This transformation highlights curvature changes in the signal, making it particularly useful for identifying inflection points, overlapping peaks, and subtle baseline variations in the spectral data. Absorption at 1688 nm, 1730 nm, 2043 nm, 2175 nm, and 2275 nm, labeled as “c”, are associated with collagen yield. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<table><caption>**Table 4**

Peak positions and common NIR absorption bands.</caption><thead><tr><th>Waveband</th><th>Range Minimum</th><th>Range Maximum</th><th>Major Contributions and Vibration Types</th></tr></thead><tbody><tr><td>1688 nm</td><td>1675 nm</td><td>1700 nm</td><td>1st overtone C-H stretching (aliphatic hydrocarbons)</td></tr><tr><td>1730 nm</td><td>1717 nm</td><td>1748 nm</td><td>1st overtone C-H stretching (aliphatic hydrocarbons)</td></tr><tr><td>2043 nm</td><td>2030 nm</td><td>2059 nm</td><td>2nd overtone C=O stretching; 2nd overtone Amide I (C=O stretching and C-N stretching); Amide B and Amide II (N-H stretching and N-H bending) combination; 1st overtone N-H stretching</td></tr><tr><td>2175 nm</td><td>2155 nm</td><td>2195 nm</td><td>2nd overtone Amide II (N-H bending and C-N stretching); Amide I and Amide II combination bands (C=O stretching and N-H bending)</td></tr><tr><td>2275 nm</td><td>2250 nm</td><td>2300 nm</td><td>C-H stretch and deformation combinations</td></tr></tbody></table>

<table><caption>**Table 5**

Results of PLSR models.</caption><thead><tr><th>Range</th><th>R<sup>2</sup></th><th>RMSE</th><th>VR<sup>2</sup></th><th>VRMSE</th><th>Factors</th></tr></thead><tbody><tr><td>2030–2060, 2244–2300</td><td>0.9133</td><td>1.3987</td><td>0.8892</td><td>1.5855</td><td>3</td></tr><tr><td>2000–2300</td><td>0.9023</td><td>1.4844</td><td>0.8949</td><td>1.5396</td><td>2</td></tr><tr><td>1641–1748, 2000–2300</td><td>0.9006</td><td>1.4970</td><td>0.8966</td><td>1.5268</td><td>2</td></tr><tr><td>1641–1748, 2030–2060</td><td>0.8995</td><td>1.5052</td><td>0.8840</td><td>1.6442</td><td>2</td></tr><tr><td>2030–2060</td><td>0.8878</td><td>1.5911</td><td>0.8851</td><td>1.6164</td><td>1</td></tr><tr><td>780–2500</td><td>0.8873</td><td>1.5942</td><td>0.8704</td><td>1.7314</td><td>3</td></tr><tr><td>2244–2300</td><td>0.8822</td><td>1.6302</td><td>0.8847</td><td>1.5927</td><td>2</td></tr><tr><td>2152–2197, 2244–2300</td><td>0.8821</td><td>1.6309</td><td>0.8856</td><td>1.5895</td><td>2</td></tr><tr><td>2030–2060, 2152–2197</td><td>0.8809</td><td>1.6391</td><td>0.8830</td><td>1.6283</td><td>1</td></tr><tr><td>2152–2197</td><td>0.8641</td><td>1.7503</td><td>0.8721</td><td>1.6944</td><td>1</td></tr><tr><td>1671–1748</td><td>0.8512</td><td>1.8318</td><td>0.8429</td><td>1.8605</td><td>2</td></tr></tbody></table>

independent validation dataset. The Random Forest model was built using the Gore Lab Waves R Package (Hershberger et al., 2021).

### 2.4.4. Model validation

Due to sample size constraints after balancing the calibration dataset, we adopted a tailored approach to splitting the dataset into calibration and validation sets. The calibration set includes 100 independent samples encompassing the collagen yield distribution (0–17.2 %), and the validation set consists of 40 independent samples spanning collagen yields from 0 to 16.5 %.

To evaluate PLSR and Random Forest model performance, it is essential to define clear statistical benchmarks relevant to modeling NIR spectra from biological materials, with the coefficient of determination (R²) and root mean square error (RMSE) serving as key evaluation metrics. An R² value above 0.80 in calibration datasets and 0.75 in prediction datasets is generally considered acceptable (Malley et al., 2015; Williams and Norris, 1987). Additionally, cross-validation R² should be closely aligned with the calibration dataset R², as a significant deviation may indicate overfitting, when a model predicts noise or memorizes the training data (Ciurczak et al., 2021; Gowen et al., 2014; Martens and Dardenne, 1998). RMSE provides a more reliable evaluator of model performance than R², as it directly quantifies prediction error. Ideally, a model will exhibit low RMSE with similar values across calibration, cross-validation, and prediction datasets (Pasquini, 2018).

The number of latent variables, or factors, selected in a PSLR model is also a key evaluator of model performance. PLSR operates by maximizing the covariance between the spectra and response variables to identify latent variables that best capture the underlying relationships (Ozaki et al., 2020). The optimal number of latent variables is determined through cross-validation to ensure the model accurately reflects the relationship between the NIR spectra and response variable while avoiding overfitting (Jouan-Rimbaud et al., 1995). In this study, the goal
is to identify a model that demonstrates strong performance based on R² and RMSE, and if multiple models show similar performance, the most parsimonious model with the fewest latent variables is selected as the preferred model.

The selected calibration model is then applied to the validation set to predict collagen yield and assign specimens to the above or below 3 % groups. After validating the models using an independent validation dataset, we merge the calibration and validation datasets to build models to achieve larger calibration set sizes with improved collagen yield distribution. These combined models are then validated with a leave-one-out cross-validation. Note that although we present a preferred model here, when applying the method to unknown collections, it is standard practice to evaluate multiple models to ensure robust predictions.

Independent validation and cross-validation are essential first steps in model validation; however, the most critical test of a model's robustness lies in its application to external datasets. We validated the combined PLSR and Random Forest models using a separate set of samples from Zafarraya, an archaeological site that had previously undergone collagen extraction and AMS radiocarbon dating. The radiocarbon chronology of Zafarraya, a Pleistocene cave site in Spain, holds great significance, including some of the latest surviving members of *H. neanderthalensis* (Hublin et al., 1995; Michel et al., 2013; Wood et al., 2013). Previous work documenting low overall collagen preservation at the site prompted a considerable effort to identify samples suitable for reliable radiocarbon dating (Jacob et al., 2018; Michel et al., 2013; Wood et al., 2013). Jacob and colleagues (2018) prescreened the Zafarraya collection with %N and found a threshold of 0.76 %N correctly predicted collagen yield (above or below 1 %) in 72 % of the prescreened Zafarraya samples (n = 50). We collected and analyzed NIR spectra from a subset of the Zafarraya samples (n = 36) to evaluate NIR prescreening on a collection of known collagen yield.

### 2.4.5. Sources of error

Two primary sources of uncertainty affect the translation of predicted collagen yields into sampling decisions for AMS radiocarbon dating. First, the collagen extraction protocol itself introduces variability, with interlaboratory comparisons indicating a typical precision of ±1.5 % collagen yield (Pestle et al., 2014b). This is highly correlated to the inhomogeneous preservation of collagen within a bone (see Jacob et al., 2018). It is therefore impossible to rigorously define an uncertainty that applies to all bones. Second, the predictive model has inherent error, discussed in detail below. These uncertainties are incorporated into all sampling decisions. To maximize the likelihood of analytical success, we typically apply a conservative threshold of 3 % predicted collagen yield to ensure samples have a high probability of exceeding the 1 % minimum yield required for reliable dating (Van Klinken, 1999). This threshold is not absolute; depending on the collection, research objectives, and sample availability, specimens with predicted yields below 3 % may still be selected for analysis.

## 3. Results

### 3.1. PLSR calibration and validation

Table 5 presents the collagen prediction model statistics from the PLSR models at 10 wavelength combinations. The validation results are also presented in Table 5, reported as validation R² (VR²) and root mean square error of validation (RMSEV). While several models demonstrated strong predictive performance, the preferred model (i.e., near best error statistics combined with fewest latent variables) was built using the restricted spectral range 2030–2060 nm, referred to as the 2045 model. The PLSR in the 2045 nm range resulted in a one-factor model that predicted the percent collagen well (R² = 0.89, RMSEC = 1.59; Fig. S4). Although other models, such as the one-factor model built using the range 2030–2060 nm and 2152–2197 nm (R² = 0.88, RMSEC = 1.64) or

three-factor model built using 2030–2060 nm and 2244–2300 nm (R² = 0.91, RMSEC = 1.40) also performed well, the 2045 PLSR model's comparable error values and more limited spectral range make it slightly more efficient and parsimonious. Notably, the 2045 nm region largely avoids wavelengths associated with the absorbance of common consolidants, which enhances the model's applicability to diverse archaeological collections (see discussion of consolidants below). When applied to the validation set, the 2045 model correctly classifies 20/24 samples, yielding under 3 %, and 14/16 samples yielding above 3 %, with a root mean square error of prediction (RMSEP) of 1.62 (Fig. S5). Notably, among the four misclassified samples predicted to contain over 3 % collagen, all yielded more than 1 % collagen, meeting the minimum threshold for radiocarbon dating and paleodietary analysis. Replicate predictions from ten scans at a single location showed low variability (standard deviation = 0.03 %, coefficient of variation = 0.85 %), indicating high repeatability of the NIR measurements.

### 3.2. Random Forest calibration and validation

We selected the 780–2500 nm Random Forest model based on the smallest RMSE and incorporated 108 randomly selected predictors (Fig. S6). The model demonstrated high predictive accuracy with an RMSEC of 1.47 and an R² of 0.99, indicating a strong correlation between predicted and observed collagen values (Fig. S7). The 20 most important variables in the Random Forest model are listed in Fig. S8. We determined the number of predictor variables randomly selected at each split in the 2045 nm Random Forest model with the configuration that produced the lowest RMSE (Fig. S9). The 2045 nm Random Forest model demonstrated improved predictive accuracy with an RMSE of 0.72 and an R² of 0.98 (Fig. S10). The 20 most important variables included in the 2045 nm Random Forest model are listed in Fig. S11.

When applied to the validation set, the 780–2500 nm Random Forest model correctly classifies 21/24 samples, yielding under 3 % and 15/16 samples yielding above 3 % with a root mean square error of validation (RMSEV) of 1.34 (Fig. S7). The 2045 Random Forest model correctly classifies 23/24 samples yielding under 3 % and 16/16 samples above 3 % with an RMSEV of 0.70 (Fig. S7).

### 3.3. Combined models

Using the combined dataset of 140 samples, a PLSR model built using 2030 nm–2060 nm predicted collagen yield well (R² = 0.89, RMSEC = 1.60; Fig. 2) and performed well in the leave-one-out cross-validation (VR² = 0.89 and RMSEV = 1.62; Fig. 2). The combined Random Forest model built using NIR spectra (503 variables; Fig. 3A) of the combined dataset and a leave-one-out cross-validation demonstrates strong performance with an RMSEC of 1.38, an R² of 0.99 (Fig. 3B). The 20 most important variables included in the combined Random Forest model are listed in Fig. 3C. The combined 2045 nm Random Forest model built using 2030–2060 nm (2 variables; Fig. 3D) and a leave-one-out cross-validation demonstrates strong performance with an RMSEC of 1.53 and an R² of 0.98 (Fig. 3E). The 20 most important variables included in the combined Random Forest model are listed in Fig. 3F.

### 3.4. Zafarraya results

NIR absorbance spectra of the Zafarraya and calibration samples revealed distinct absorption features at 2135 nm, 2250 nm, and 2296 nm in several Zafarraya samples (Fig. 4A). These peaks correspond to known absorption regions of polyvinyl acetate (PVA), a common consolidant documented in the sample records. Reference spectra collected from PVA-treated samples confirm these wavelengths as diagnostic of PVA contamination. To further investigate, a PCA was conducted on the calibration dataset combined with the 36 Zafarraya samples, incorporating collagen- and PVA-associated regions (2000–2300 nm; Fig. 4B). The PCA identified several Zafarraya samples as outliers along PC-2,
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Predicting collagen preservation using 2030–2060 nm. The results of the PLSR show predicted collagen yields versus actual collagen yields for 140 calibration samples. Note that slightly negative predictions occur when the linear regression equation extrapolates beyond the range of the calibration data; these values are mathematical artifacts and do not indicate the presence of negative collagen.</figcaption></figure>

which accounts for 14 % of the total spectral variation; these samples exceed Hotelling's T² threshold of 6.22, corresponding to the 95 % confidence limit. Notably, multiple specimens known to be contaminated with consolidants were among these outliers, supporting the conclusion that contamination contributes to the increased spectral variation. Solvent treatment was applied to 15 of the 36 samples based on visual indications of PVA contamination, and many of these solvent-treated samples cluster among the PCA outliers. Additionally, two untreated samples exhibited strong absorbance at PVA-associated wavelengths, indicating that likely contamination was not detected visually.

To evaluate model performance, we excluded the two untreated samples showing strong PVA absorbance and the solvent-treated samples, leaving 19 Zafarraya samples. For the Zafarraya samples, we applied both the combined versions of the PLSR and Random Forest models to assess collagen preservation.

### 3.5. PLSR models

The combined PLSR model has an RMSEP of 2.51 when predicting collagen yields of the Zafarraya specimens. Using a threshold of 3 % predicted collagen, 18/19 (94.74 %) of the Zafarraya specimens are correctly classified according to sufficient or insufficient yields for radiocarbon dating (Table 6).

### 3.6. Random Forest models

When applied to the Zafarraya samples, the 780–2500 nm Random Forest model yielded a RMSEP of 1.64 and correctly classified 11/19 samples (57.89 %) using a 3 % collagen yield threshold (Table 7). Classification accuracy improves to 17/19 samples (89.47 %) when using a Random Forest model built with the 2030–2060 nm spectral range and the same 3 % threshold, although this model exhibited a slightly higher RMSEP of 2.11 (Table 8).

## 4. Discussion

### 4.1. Model development

The calibration PLSR model, developed using the 2030–2060 nm spectral range and a Savitzky-Golay transformation (second derivative, third-order polynomial, 31 points smoothing), demonstrated strong performance during independent validation. It was successfully validated against a validation set, confirming its reliability for collagen quantification in archaeological bone. The 2045 nm PLSR model's RMSEC (1.59), along with the RMSEV from the validation (1.62), both remain below 2 %, supporting confidence in the established 3 % threshold, which accounts for both intra- and inter-lab variation of 1.5 % (Pestle et al., 2015). The calibration Random Forest models consistently produce lower RMSE in the calibration and validation datasets than the PLSR model. The Random Forest models also outperformed the PLSR model on the validation set, correctly classifying 36/40 (780–2500 nm Random Forest model) and 39/40 (2045 Random Forest model) samples compared to 34/40 correctly classified by the 2045 PLSR model using the 3 % threshold.

### 4.2. Zafarraya

Among the Zafarraya samples, the 2045 nm PLSR model outperformed both the 780–2500 nm and 2045 Random Forest models in selecting samples with sufficient collagen yield for radiocarbon dating, although the Random Forest models consistently produced lower RMSEP values. The decreased performance of the 780–2500 nm Random Forest model is likely driven by overfitting, as the model included 503 randomly selected predictors. Furthermore, in the 780–2500 nm Random Forest model, several of the most important variables overlap with the spectral regions of PVA absorbance (Fig. 3C). Despite efforts to select uncontaminated Zafarraya specimens, it is possible that samples with trace amounts of PVA contamination contributed to the misclassification observed in the 780–2500 nm Random Forest model. Nevertheless, in the absence of contamination, the 780–2500 nm Random Forest model demonstrates strong performance, underscoring the model's robustness and efficiency under optimal conditions. For comparison, applying a PLSR model built using the full 780–2500 nm range to the Zafarraya dataset results in a similar decrease in classification success, correctly identifying only 14/19 samples. When contaminant use is unknown or suspected, a key advantage of PLSR or Random Forest models built using the 2030–2060 nm spectral region is that they avoid wavelengths where PVA and other contaminants in our reference library absorb.

Restricting the spectral range to 2030–2060 nm produced PLSR and RF models with comparable predictive ability, emphasizing that both approaches are robust under conditions most relevant to archaeological applications. Regardless, the 2045 nm PLSR and 2045 nm Random Forest models outperformed previously published %N in correctly classifying samples as suitable or unsuitable for radiocarbon dating (Jacob et al., 2018). Although %N correctly classified 72 % of samples at Zafarraya (Jacob et al., 2018), %N correctly classified 12/19 (63.16 %) samples included here compared to 17/19 (89.47 %) using the 2045 nm Random Forest model and 18/19 (94.74 %) using the 2045 nm PLSR model (Table 9).

### 4.3. Consolidants

Contamination among the Zafarraya collection complicates the interpretation of the NIR spectra, and the solvent wash also introduces additional challenges. Solvent washes are designed to remove consolidants before analysis but are also known to degrade collagen, particularly when combined with prolonged heating or ultrasonic treatment (Brock et al., 2018; Gopinath et al., 2014; Moreiras Reynaga et al., 2022). For instance, among the Zafarraya samples, the
780-2500 nm Combined Random Forest

<figure><img src="image_6.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_8.png" /></figure>

2030-2060 nm Combined Random Forest

<figure><img src="image_9.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_11.png" /></figure>

(caption on next page)
**Fig. 3.** Combined Random Forest Models. (A) Combined 780–2500 nm Random Forest RMSE and number of randomly selected predictors. RMSE from repeated cross-validations plotted against the number of randomly selected predictor variables. The RMSE decreases sharply and stabilizes around 503 predictor variables. (B) Predicted collagen yield from the combined Random Forest model with a leave-one-out cross-validation vs. reported collagen values. The combined Random Forest model explains 91.47 % of the total variation in the data and has an RMSEP of 1.38. (C) Important variables included in the combined Random Forest model. (D) Random Forest RMSE and number of randomly selected predictors. RMSE from repeated cross-validations plotted against the number of randomly selected predictor variables. The lowest RMSE is found at two randomly selected predictors. (E) Predicted collagen yield from the combined 2045 Random Forest model with a leave-one-out cross-validation vs. reported collagen values. The combined Random Forest model explains 89.47 % of the total variation in the data and has an RMSEC of 1.53. (F) Important variables included in the combined 2045 nm Random Forest model.

<figure><img src="image_12.png" /></figure>

**Fig. 4.** NIR absorbance spectra of Zafarraya specimens. A) NIR absorbance spectra (second derivative; third-order polynomial; 31 points smoothing) of known archaeological specimens yielding 17.10 % collagen (maroon), 9.26 % collagen (red), 6.00 % collagen (dark blue), 3.22 % collagen (light blue), 0.01 % collagen (magenta), and unknown Zafarraya samples (gray). Absorption at 1688 nm, 1730 nm, 2043 nm, 2175 nm, and 2275 nm, labeled as C, is associated with collagen yield, and absorption at 2135 nm, 2250 nm, and 2296 nm, labeled PVA, is associated with consolidant contamination. Among known reference samples, PVA absorbs strongly at 2135 nm, 2250 nm, and 2296 nm, making absorption at these wavelengths indicative of PVA contamination. B) PCA of Zafarraya and calibration samples in the collagen- and PCA-associated range of NIR (2000–2300). The samples cluster according to collagen yield along PC-1 (84 % variation): higher yielding samples (triangles; 11.5–17.2 %), moderate yielding (circles; 5.7–11.5 %), and lower yielding samples (squares; 0–5.7 %). The square samples indicated in red are Zafarraya samples treated with a solvent. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

solvent-treated samples exhibit a lower average collagen content (1.05 % weight collagen) compared to untreated samples (1.89 % weight collagen). While this difference is relatively small, it certainly impacts samples with low collagen preservation, where any additional degradation can impact analytical outcomes. When consolidant contamination is suspected, NIR spectroscopy is a valuable tool for detecting the presence of consolidants. Regardless of the modeling approach, data exploration, including individual spectra analysis and PCAs, will always be required to detect potential spectral anomalies and ensure accurate

predictions.

Identifying specific contaminants is invaluable, as removing all contamination is essential before performing most archaeometric analyses, and each contaminant requires a specific solvent for removal (Brock et al., 2018). Consolidant application is often undocumented, especially in legacy museum collections, and even when records exist, the particular bones treated are rarely identified. While the presence of a contaminant can be detectable through visual inspection, identifying the exact contaminant requires FTIR (e.g., (Brock et al., 2018; D'Elia et al.,
<caption>**Table 6**</caption>

Zafarraya combined 2045 nm PLSR predictions with a 3 % threshold and an AMS radiocarbon dating threshold.

<table><caption>**Table 7**</caption><thead><tr><th></th><th>Predicted to yield over 3 %</th><th>Predicted to yield less than 3 %</th><th>Totals</th></tr></thead><tbody><tr><td>Sufficient for Radiocarbon Dating (&gt;1 %)</td><td>11</td><td>0</td><td>11</td></tr><tr><td>Insufficient for Radiocarbon Dating (&lt;1 %)</td><td>1</td><td>7</td><td>8</td></tr><tr><td>Totals</td><td>12</td><td>7</td><td>19</td></tr></tbody></table>

Zafarraya combined 780–2500 nm Random Forest predictions with a 3 % threshold and an AMS radiocarbon dating threshold.

<table><caption>**Table 8**</caption><thead><tr><th></th><th>Predicted to yield over 3 %</th><th>Predicted to yield less than 3 %</th><th>Totals</th></tr></thead><tbody><tr><td>Sufficient for Radiocarbon Dating (&gt;1 %)</td><td>3</td><td>7</td><td>10</td></tr><tr><td>Insufficient for Radiocarbon Dating (&lt;1 %)</td><td>1</td><td>8</td><td>9</td></tr><tr><td>Totals</td><td>4</td><td>15</td><td>19</td></tr></tbody></table>

Zafarraya combined 2045 nm Random Forest predictions with a 3 % threshold and an AMS radiocarbon dating threshold.

<table><caption>**Table 9**</caption><thead><tr><th></th><th>Predicted to yield over 3 %</th><th>Predicted to yield less than 3 %</th><th>Totals</th></tr></thead><tbody><tr><td>Sufficient for Radiocarbon Dating (&gt;1 %)</td><td>10</td><td>1</td><td>11</td></tr><tr><td>Insufficient for Radiocarbon Dating (&lt;1 %)</td><td>1</td><td>7</td><td>8</td></tr><tr><td>Totals</td><td>11</td><td>8</td><td>19</td></tr></tbody></table>

Previously published %N and AMS radiocarbon dating threshold results (Jacob et al., 2018).

<table><thead><tr><th></th><th>&gt;0.76 %<br>Nitrogen</th><th>&lt;0.76 %<br>Nitrogen</th><th>Totals</th></tr></thead><tbody><tr><td>Sufficient for Radiocarbon Dating (&gt;1 %)</td><td>5</td><td>6</td><td>11</td></tr><tr><td>Insufficient for Radiocarbon Dating (&lt;1 %)</td><td>1</td><td>7</td><td>8</td></tr><tr><td>Totals</td><td>6</td><td>13</td><td>19</td></tr></tbody></table>

2007), Raman spectroscopy (e.g., (Ohlídalová et al., 2006), or pyrolysis-GC/MS (e.g., (Deviése et al., 2021, 2019; Nishimoto, 2011; Ostapkowicz et al., 2017). Further research advancing NIR with contamination detection has the potential to both identify specific contaminants, provided they absorb in the NIR range, and facilitate the selection of predictive models that exclude spectral regions influenced by such contamination. This will help ensure collagen predictions remain accurate and reliable despite the presence of post-depositional treatments or environmental contaminants.

## 5. Conclusions

To develop our calibration dataset, we collected whole bone samples from a broad range of temporal and geographic archaeological sites, creating a more robust and broadly applicable model than that presented in Sponheimer et al. (2019). Our NIR models demonstrate strong predictive performance, with RMSE values typically between 1.5 % and

2.0 %, and achieve classification accuracies exceeding those based on % N, which remains the most widely used proxy for collagen preservation.

Still, even when the same extraction protocol is followed, subtle variations in intra- and inter-laboratory collagen extraction techniques introduce an estimated 1.5 % variation in yield (Pestle et al., 2014b), meaning our predictive models cannot be expected to perform with greater accuracy than the underlying experimental variability permits. We limited inter-lab variability by focusing on collagen extraction protocols developed for AMS radiocarbon dating, but intra-lab variation remains a concern. Even minor inconsistencies in demineralization duration, solution concentrations, and temperature can introduce artificial variations in collagen yield, which do not reflect the actual preservation state of the bone and can impact model performance.

Further, the heterogeneous distribution of collagen within a bone, well-established in prior studies (Anné et al., 2019; Jacob et al., 2018; Lugli et al., 2021), concomitant with the limitation of collecting NIR spectra post-collagen extraction, introduces an inherent source of error in our collagen prediction models. The previously extracted portion may not accurately represent the preservation of the scanned portion. For example, the decreased performance of the PLSR and Random Forest models among the Zafarraya samples compared to the validation set is likely due to variable collagen preservation documented among bone from the Zafarraya collection (Jacob et al., 2018; Wood, 2011; Wood et al., 2013). To minimize variability, the analyzed portion of the bone must correspond precisely to the portion sampled for destructive analyses. To control for variability during model development, the spectrum included in calibration is typically the scan closest to the previous collagen extraction sampling site. Further, averaging collagen predictions from two to three non-overlapping locations (more for larger bones) enhances reliability by selecting bones that are well preserved overall.

Although %N, FTIR spectroscopy, and Raman spectroscopy have shown promise in archaeological applications, their effectiveness is often limited by the need for sample preparation. In contrast, NIR spectroscopy combines high predictive accuracy with deep penetration and rapid, non-destructive analysis, making it particularly well-suited for large-scale prescreening of archaeological bone. The greater penetration depth of NIR light into bone allows for more representative sampling of bulk collagen content, improving the correlation between spectral data and actual collagen yields.

Future efforts will focus on developing models tailored to additional collagen pretreatment protocols, such as XAD resin protocols and protocols employed in stable isotope analysis. Our current models, which are built using radiocarbon ultrafiltration protocols, likely underestimate collagen yields for non-ultrafiltered protocols. We also plan to conduct future experimental work to assess the model's sensitivity to low collagen preservation (less than 1 %). The planned experimental work aims to define the boundaries of the models' predictive capabilities, enhance sensitivity to detect low-collagen samples, and determine collagen thresholds suitable for successful ZooMS analysis. The predictive models will never be perfect; however, they can still provide valuable insights when applied to well-defined, practical questions. Overall, single-point NIR spectroscopy is a valuable, complementary tool for archaeological bone analysis, offering actionable insights that complement established methods.

## CRediT authorship contribution statement

**Christina Ryder:** Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Gerardo Celis:** Writing – review & editing, Visualization, Methodology. **Thibaut Devièse:** Writing – review & editing, Writing – original draft, Conceptualization. **Sahra Talamo:** Writing – review & editing, Resources. **Katerina Douka:** Writing – review & editing, Resources, Conceptualization. **Matthew Collins:** Writing – review & editing, Resources, Conceptualization.
review & editing, Resources. **Angela Perri:** Writing – review & editing, Resources. **Heather Thakar:** Writing – review & editing, Resources. **William Pestle:** Writing – review & editing, Resources. **Matt Sponheimer:** Writing – review & editing, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.

## GitHub

We thank Dr. Carrignon for the reproducibility review and constructive feedback. In response, we have uploaded the full source files for the analysis, along with the input data and a detailed README file that describes the file structure, dependencies, and workflow. Absolute paths have been removed and replaced with relative references for improved portability. The code and data have been deposited in this GitHub repository to facilitate access and long-term reproducibility. We appreciate the reviewer's suggestions and have implemented these revisions to align with best practices for open and reproducible research.

GitHub Repository: https://github.com/gerlis22/NIR_Collagen.

## Data availability statement

The predictive models are available from the corresponding author upon request; however, their application to data collected with different instruments should be approached with caution, as device-to-device calibration is typically required. All raw spectral data are provided in the Supplemental Materials.

## Funding information

The research was funded through grants awarded to Christina Ryder by the University of Colorado Boulder (Department of Anthropology and the Center to Advance Research and Teaching in the Social Sciences), the Leakey Foundation, and the Wenner-Gren Foundation for the project "Saving old bones: using near-infrared spectroscopy to predict collagen yield in bone.

## Reproducible results

The Associate Editor for Reproducibility could download all materials and reproduce the results presented by the authors.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jas.2025.106448.

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